A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning

Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnost...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Discover applied sciences Ročník 7; číslo 6; s. 528 - 34
Hlavní autori: Bijoy, Md. Hasan Imam, Mia, Md. Jueal, Rahman, Md. Mahbubur, Arefin, Mohammad Shamsul, Dhar, Pranab Kumar, Shimamura, Tetsuya
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 23.05.2025
Springer Nature B.V
Springer
Predmet:
Abnormalities
Accuracy
Algorithms
Applications programs
Applied and Technical Physics
Automation
Biomarkers
Cardiovascular diseases
Chemistry/Food Science
Chronic kidney disease
Classification
Computing time
Datasets
Decision trees
Deep learning
Earth Sciences
End-stage renal disease
Engineering
Ensemble learning
Environment
Feature selection
Internet of medical things
IoMT
Kidney diseases
Kidneys
Learning algorithms
Machine learning
Materials Science
MKR stacking
MKR Voting
Mobile communications networks
Mobile computing
Multilayer perceptrons
Neural networks
Predictions
R&D
Real time
Renal function
Research & development
Robot learning
RPA
South Asia
RPA
Chronic kidney disease
MKR Voting
MKR stacking
Machine learning
IoMT
ISSN:3004-9261, 2523-3963, 3004-9261, 2523-3971
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications.
AbstractList Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications.
Abstract Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications.
ArticleNumber 528
Author Bijoy, Md. Hasan Imam
Arefin, Mohammad Shamsul
Mia, Md. Jueal
Rahman, Md. Mahbubur
Shimamura, Tetsuya
Dhar, Pranab Kumar
Author_xml – sequence: 1
  givenname: Md. Hasan Imam
  surname: Bijoy
  fullname: Bijoy, Md. Hasan Imam
  email: hasan15-11743@diu.edu.bd
  organization: Department of Computer Science and Engineering, Daffodil International University
– sequence: 2
  givenname: Md. Jueal
  surname: Mia
  fullname: Mia, Md. Jueal
  organization: Department of Computer Science and Engineering, Daffodil International University
– sequence: 3
  givenname: Md. Mahbubur
  surname: Rahman
  fullname: Rahman, Md. Mahbubur
  organization: Department of Computer Science and Engineering, Daffodil International University
– sequence: 4
  givenname: Mohammad Shamsul
  surname: Arefin
  fullname: Arefin, Mohammad Shamsul
  email: sarefin@cuet.ac.bd
  organization: Department of Computer Science and Engineering, Chittagong University of Engineering and Technology
– sequence: 5
  givenname: Pranab Kumar
  surname: Dhar
  fullname: Dhar, Pranab Kumar
  organization: Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Faculty of Science and Engineering, Waseda University
– sequence: 6
  givenname: Tetsuya
  surname: Shimamura
  fullname: Shimamura, Tetsuya
  organization: Faculty of Science and Engineering, Waseda University, Department of Information and Computer Sciences, Saitama University
BookMark eNp9kc1uHCEQhFHkSLEdv0BOSDmP08PPMBwty44tWcolOSMGmjXrWVjD7GHf3mQninPKCVRdX4G6LshZygkJ-dLDdQ-gvlXBhGQdMNnBoEfo9AdyzgFEp9nQn_1z_0Suat0CAOeglNTn5HhDS57yQvclO6yV2sOSd3aJOdHJVvR0l6c4I7X7_RzdOgi5ULRlPjYKfXQnMQfqnktO0dGX6BMeqY8VWwQ91Jg2dGfdc0xI50amJnwmH4OdK179OS_Jr_u7n7cP3dOP74-3N0-d4yNbOqEZegWOOcFCLy0L0gvvrQ8SB9RyEnoAp90ofO_lwGToFQ6CMcchKCX4JXlcc322W7MvcWfL0WQbzUnIZWNsWaKb0XiBk1Ua-eSDYL3WjrNRcGE5t1qAa1lf16y2rdcD1sVs86Gk9n3DGYxMKj6MzcVWlyu51oLh76s9mN-NmbUx0xozp8aMbhBfodrMaYPlPfo_1BvqdZwE
Cites_doi 10.1038/s41598-024-63292-5
10.1038/s41598-019-46074-2
10.1155/2021/1004767
10.1109/ACCESS.2020.2995310
10.1186/S40537-024-01050-0
10.1109/ACCESS.2020.2971208
10.3390/bdcc7030144
10.2147/RMHP.S319405
10.1007/S42454-022-00040-Y
10.12720/jait.14.2.384-391
10.1016/j.artmed.2022.102431
10.1016/J.KISU.2021.11.003
10.1109/JTEHM.2021.3073629
10.1155/2022/9898831
10.1186/S12882-018-1072-5/TABLES/5
10.3390/ELECTRONICS12010212
10.1038/s41598-022-12316-z
10.3390/diagnostics12010116
10.1038/s41598-024-78498-w
10.1109/JTEHM.2023.3234207
10.1136/BMJDRC-2021-002364
10.1016/j.xkme.2024.100804
10.1007/s12325-020-01568-8
10.1016/j.ijcce.2023.12.002
10.2196/18585
10.1016/j.jclinepi.2020.03.002
10.24432/C5G020
10.3390/bioengineering9080350
10.1007/s10489-021-02550-9
10.4108/eai.13-8-2021.170671
10.1007/s44174-022-00027-y
10.1007/S40620-022-01302-3/TABLES/9
10.1109/ACCESS.2021.3129491
10.1038/s41581-024-00828-y
10.1016/j.dajour.2023.100169
10.1186/S12882-021-02474-Z/FIGURES/5
10.1038/s41581-024-00820-6
10.1016/j.imu.2021.100631
10.1016/J.IRBM.2020.07.002
10.1007/S11255-024-04281-5/METRICS
10.53730/ijhs.v6nS2.5579
10.1007/978-3-031-11570-7_17
10.3390/healthcare9050546
10.1016/j.iswa.2022.200144
10.2147/RMHP.S346856
10.1016/S2589-7500(20)30063-7
10.1504/IJMEI.2019.104981
10.1016/j.eswa.2023.119851
10.1007/s11192-009-0008-z
10.1007/978-3-031-11570-7_26
10.1038/s41598-022-26160-8
10.1007/S11042-023-15188-1/TABLES/3
10.1016/j.jobcr.2021.11.010
10.1016/S2214-109X(23)00570-3
10.1080/10255842.2023.2181660
10.1007/978-981-16-7597-3_38
10.1007/978-3-030-01560-2_5
10.4103/jmss.JMSS_13_20
10.1007/s11042-024-20205-y
10.1038/s41598-024-74993-2
10.1007/s10479-018-2818-y
10.1080/1206212X.2023.2262786
10.3390/info14100542
10.1186/S12882-020-02151-7/FIGURES/4
10.1109/JSEN.2020.3034904
10.1016/j.bspc.2023.105368
10.1038/s41581-022-00671-z
10.1038/s41581-019-0191-y
10.1007/978-3-030-04061-1_18
10.3390/s22072625
10.3390/ijerph19010226
10.1177/20552076231224225
10.1155/2021/2487759
10.1016/j.cmpb.2021.106329
10.1002/IMA.70016
10.1109/LSENS.2019.2942145
10.1007/978-981-16-6636-0_1
10.1155/2023/9266889
10.3390/app12073673
10.1111/COIN.12696
10.1088/1742-6596/2020/1/012047
10.1016/j.bbrc.2022.02.099
10.1109/ACCESS.2021.3133700
ContentType Journal Article
Copyright The Author(s) 2025
Copyright Springer Nature B.V. Jun 2025
Copyright_xml – notice: The Author(s) 2025
– notice: Copyright Springer Nature B.V. Jun 2025
DBID C6C
AAYXX
CITATION
3V.
7XB
88I
8FE
8FG
8FK
ABJCF
ABUWG
AEUYN
AFKRA
ATCPS
AZQEC
BENPR
BGLVJ
BHPHI
BKSAR
CCPQU
COVID
D1I
DWQXO
GNUQQ
HCIFZ
KB.
L6V
M2P
M7S
PATMY
PCBAR
PDBOC
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
PYCSY
Q9U
DOA
DOI 10.1007/s42452-025-06980-9
DatabaseName Springer Nature OA Free Journals
CrossRef
ProQuest Central (Corporate)
ProQuest Central (purchase pre-March 2016)
Science Database (Alumni Edition)
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni) (purchase pre-March 2016)
Materials Science & Engineering Collection
ProQuest Central (Alumni)
One Sustainability
ProQuest Central UK/Ireland
Agricultural & Environmental Science Collection
ProQuest Central Essentials
AUTh Library subscriptions: ProQuest Central
Technology Collection
Natural Science Collection
Earth, Atmospheric & Aquatic Science Collection
ProQuest One Community College
Coronavirus Research Database
ProQuest Materials Science Collection
ProQuest Central Korea
ProQuest Central Student
SciTech Premium Collection
Materials Science Database
ProQuest Engineering Collection
Science Database
Engineering Database
Environmental Science Database
Earth, Atmospheric & Aquatic Science Database
Materials Science Collection
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
Engineering Collection
Environmental Science Collection
ProQuest Central Basic
Open Access: DOAJ - Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
ProQuest Central Student
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
Materials Science Collection
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Central
Earth, Atmospheric & Aquatic Science Collection
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
ProQuest Engineering Collection
Natural Science Collection
ProQuest Central Korea
Agricultural & Environmental Science Collection
Materials Science Database
ProQuest Central (New)
Engineering Collection
ProQuest Materials Science Collection
Engineering Database
ProQuest Science Journals (Alumni Edition)
ProQuest Central Basic
ProQuest Science Journals
ProQuest One Academic Eastern Edition
Earth, Atmospheric & Aquatic Science Database
Coronavirus Research Database
ProQuest Technology Collection
ProQuest SciTech Collection
Environmental Science Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
Environmental Science Database
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: KB.
  name: Materials Science Database
  url: http://search.proquest.com/materialsscijournals
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 3004-9261
2523-3971
EndPage 34
ExternalDocumentID oai_doaj_org_article_d4eba79e3bdf42199c328434a33a940c
10_1007_s42452_025_06980_9
GeographicLocations South Asia
GeographicLocations_xml – name: South Asia
GroupedDBID AAJSJ
AASML
ADMLS
ALMA_UNASSIGNED_HOLDINGS
C6C
GROUPED_DOAJ
M~E
SOJ
AAYXX
BGNMA
CITATION
EBLON
M4Y
NU0
0R~
3V.
7XB
88I
8FE
8FG
8FK
AAHNG
AAKKN
ABDZT
ABECU
ABEEZ
ABFTV
ABHQN
ABJCF
ABKCH
ABMQK
ABTMW
ABUWG
ABXPI
ACACY
ACMLO
ACOKC
ACSTC
ACULB
ADKNI
ADURQ
ADYFF
AEJRE
AEUYN
AFGXO
AFKRA
AFQWF
AGDGC
AGJBK
AILAN
AITGF
AJZVZ
AMKLP
ATCPS
AXYYD
AZQEC
BAPOH
BENPR
BGLVJ
BHPHI
BKSAR
C24
CCPQU
COVID
D1I
DWQXO
EBS
FNLPD
GNUQQ
GNWQR
HCIFZ
J-C
KB.
KOV
L6V
M2P
M7S
NQJWS
OK1
PATMY
PCBAR
PDBOC
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
PYCSY
Q9U
STPWE
TSG
UOJIU
UTJUX
VEKWB
VFIZW
ZMTXR
ID FETCH-LOGICAL-c382t-492ed70c2c42f15a2f5d4ddadf5e6e95b4960c9c84d1d5625f17e6422c30f7743
IEDL.DBID DOA
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001493470000006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 3004-9261
2523-3963
IngestDate Tue Oct 14 18:45:31 EDT 2025
Wed Oct 08 14:21:02 EDT 2025
Sat Nov 29 07:55:21 EST 2025
Sat May 24 01:16:16 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 6
Keywords RPA
Chronic kidney disease
MKR Voting
MKR stacking
Machine learning
IoMT
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c382t-492ed70c2c42f15a2f5d4ddadf5e6e95b4960c9c84d1d5625f17e6422c30f7743
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://doaj.org/article/d4eba79e3bdf42199c328434a33a940c
PQID 3208257368
PQPubID 5758472
PageCount 34
ParticipantIDs doaj_primary_oai_doaj_org_article_d4eba79e3bdf42199c328434a33a940c
proquest_journals_3208257368
crossref_primary_10_1007_s42452_025_06980_9
springer_journals_10_1007_s42452_025_06980_9
PublicationCentury 2000
PublicationDate 2025-05-23
PublicationDateYYYYMMDD 2025-05-23
PublicationDate_xml – month: 05
  year: 2025
  text: 2025-05-23
  day: 23
PublicationDecade 2020
PublicationPlace Cham
PublicationPlace_xml – name: Cham
– name: London
PublicationTitle Discover applied sciences
PublicationTitleAbbrev Discov Appl Sci
PublicationYear 2025
Publisher Springer International Publishing
Springer Nature B.V
Springer
Publisher_xml – name: Springer International Publishing
– name: Springer Nature B.V
– name: Springer
References 6980_CR22
M Dadkhah (6980_CR64) 2021; 11
E Sivasankar (6980_CR33) 2019; 11
N Bhaskar (6980_CR19) 2021; 42
S Bhadra (6980_CR62) 2024
P Gogoi (6980_CR36) 2024
A Motwani (6980_CR79) 2022; 134
J-X Zheng (6980_CR83) 2024
G Chen (6980_CR27) 2020; 8
WS da Silva (6980_CR80) 2022; 273
6980_CR28
CP Kovesdy (6980_CR4) 2011; 12
SJ Maisha (6980_CR23) 2022; 348
MF Khan (6980_CR70) 2021; 20
D Swain (6980_CR35) 2023; 12
M Elhoseny (6980_CR18) 2019; 9
C Sabanayagam (6980_CR20) 2020; 2
UK Lilhore (6980_CR57) 2024; 40
A Bilal (6980_CR84) 2024; 14
H Khalid (6980_CR48) 2023
S Chitra (6980_CR66) 2022
Q Bai (6980_CR58) 2022; 12
6980_CR61
C Zoccali (6980_CR3) 2024; 20
A Bilal (6980_CR85) 2025; 12
J Janani (6980_CR26) 2021; 12
CS Yu (6980_CR78) 2020; 22
R Dwivedi (6980_CR69) 2022; 12
A Chagnac (6980_CR8) 2024; 6
6980_CR15
A Bin Sawad (6980_CR65) 2022; 22
Md Mustafizur Rahman (6980_CR77) 2024; 87
MOF Goni (6980_CR43) 2022; 95
C Kaur (6980_CR24) 2023; 14
M Murton (6980_CR14) 2021; 38
MC Groccia (6980_CR60) 2023; 14
YL Chiu (6980_CR56) 2021; 14
M Hasan (6980_CR2) 2018; 19
H Kriplani (6980_CR31) 2019; 31
V Chaurasia (6980_CR52) 2022; 4
SA Ebiaredoh-Mienye (6980_CR73) 2022; 9
S Krishnamurthy (6980_CR30) 2021; 9
S Bhadra (6980_CR63) 2024; 27
P Ventrella (6980_CR40) 2021; 209
X Cao (6980_CR55) 2022; 15
S Pal (6980_CR49) 2023; 82
A Abdelaziz (6980_CR16) 2019; 1404
JI Garaycoechea (6980_CR6) 2023; 19
MA Abdel-Fattah (6980_CR41) 2022; 2022
SD Bass (6980_CR59) 2010; 82
SI Ali (6980_CR75) 2021; 19
D Chicco (6980_CR71) 2021; 9
AK Bello (6980_CR7) 2024; 12
L Luo (6980_CR37) 2024; 14
KM Almustafa (6980_CR51) 2021; 24
M Nishat (6980_CR21) 2018
MS Arif (6980_CR50) 2023; 7
S Pal (6980_CR25) 2023; 1
FP Schena (6980_CR47) 2022; 35
M Sajid (6980_CR86) 2025; 35
R Thakur (6980_CR12) 2021; 21
K Hema (6980_CR72) 2024; 5
S Nusinovici (6980_CR42) 2020; 122
A Francis (6980_CR5) 2024; 20
S Akter (6980_CR53) 2023; 223
R Sawhney (6980_CR82) 2023; 6
S Akter (6980_CR74) 2021; 9
NJ Subashini (6980_CR39) 2023; 45
AC Silveira (6980_CR44) 2022; 12
Y Zhou (6980_CR46) 2022; 603
P Dhal (6980_CR81) 2022; 52
W Fan (6980_CR9) 2020; 294
A Sobrinho (6980_CR10) 2020; 8
A Haratian (6980_CR34) 2022; 12
HAU Rehman (6980_CR29) 2021; 2020
V Pandey (6980_CR67) 2024; 14
AS Levey (6980_CR11) 2019; 16
Md Rashed-Al-Mahfuz (6980_CR76) 2021; 9
W-H Hsieh (6980_CR68) 2023; 11
SK Dey (6980_CR32) 2022; 16
EM Senan (6980_CR13) 2021; 2021
N Bhaskar (6980_CR17) 2019; 3
V Singh (6980_CR38) 2022; 12
CC Lim (6980_CR1) 2021; 9
H Ilyas (6980_CR45) 2021; 22
M Weldegiorgis (6980_CR54) 2020; 21
References_xml – volume: 14
  start-page: 1
  issue: 1
  year: 2024
  ident: 6980_CR84
  publication-title: Sci Rep
  doi: 10.1038/s41598-024-63292-5
– ident: 6980_CR22
– volume: 9
  start-page: 1
  issue: 1
  year: 2019
  ident: 6980_CR18
  publication-title: Sci Rep
  doi: 10.1038/s41598-019-46074-2
– volume: 2021
  start-page: 1004767
  year: 2021
  ident: 6980_CR13
  publication-title: J Healthc Eng
  doi: 10.1155/2021/1004767
– volume: 8
  start-page: 100497
  year: 2020
  ident: 6980_CR27
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2995310
– volume: 12
  start-page: 1
  issue: 1
  year: 2025
  ident: 6980_CR85
  publication-title: J Big Data
  doi: 10.1186/S40537-024-01050-0
– volume: 8
  start-page: 25407
  year: 2020
  ident: 6980_CR10
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2971208
– volume: 7
  start-page: 144
  year: 2023
  ident: 6980_CR50
  publication-title: Big Data Cognit Comput
  doi: 10.3390/bdcc7030144
– volume: 14
  start-page: 4401
  year: 2021
  ident: 6980_CR56
  publication-title: Risk Manag Healthc Policy
  doi: 10.2147/RMHP.S319405
– volume: 4
  start-page: 1
  issue: 1
  year: 2022
  ident: 6980_CR52
  publication-title: Human-Intell Syst Integr
  doi: 10.1007/S42454-022-00040-Y
– volume: 14
  start-page: 384
  year: 2023
  ident: 6980_CR24
  publication-title: J Adv Inf Technol
  doi: 10.12720/jait.14.2.384-391
– volume: 134
  year: 2022
  ident: 6980_CR79
  publication-title: Artif Intell Med
  doi: 10.1016/j.artmed.2022.102431
– volume: 12
  start-page: 7
  issue: 2022
  year: 2011
  ident: 6980_CR4
  publication-title: Kidney Int Suppl
  doi: 10.1016/J.KISU.2021.11.003
– volume: 9
  start-page: 1
  year: 2021
  ident: 6980_CR76
  publication-title: IEEE J Transl Eng Health Med
  doi: 10.1109/JTEHM.2021.3073629
– volume: 2022
  start-page: 1
  year: 2022
  ident: 6980_CR41
  publication-title: Comput Intell Neurosci
  doi: 10.1155/2022/9898831
– volume: 12
  start-page: 4251
  year: 2021
  ident: 6980_CR26
  publication-title: Turk J Comput Math Educ
– volume: 19
  start-page: 1
  year: 2018
  ident: 6980_CR2
  publication-title: BMC Nephrol
  doi: 10.1186/S12882-018-1072-5/TABLES/5
– volume: 12
  start-page: 212
  year: 2023
  ident: 6980_CR35
  publication-title: Electronics
  doi: 10.3390/ELECTRONICS12010212
– volume: 12
  start-page: 1
  issue: 1
  year: 2022
  ident: 6980_CR58
  publication-title: Sci Rep
  doi: 10.1038/s41598-022-12316-z
– volume: 12
  start-page: 116
  year: 2022
  ident: 6980_CR38
  publication-title: Diagnostics
  doi: 10.3390/diagnostics12010116
– volume: 14
  start-page: 1
  issue: 1
  year: 2024
  ident: 6980_CR37
  publication-title: Sci Rep
  doi: 10.1038/s41598-024-78498-w
– volume: 11
  start-page: 375
  year: 2023
  ident: 6980_CR68
  publication-title: IEEE J Transl Eng Health Med
  doi: 10.1109/JTEHM.2023.3234207
– volume: 9
  year: 2021
  ident: 6980_CR1
  publication-title: BMJ Open Diabetes Res Care
  doi: 10.1136/BMJDRC-2021-002364
– volume: 6
  start-page: 100804
  year: 2024
  ident: 6980_CR8
  publication-title: Kidney Med
  doi: 10.1016/j.xkme.2024.100804
– volume: 38
  start-page: 180
  year: 2021
  ident: 6980_CR14
  publication-title: Adv Ther
  doi: 10.1007/s12325-020-01568-8
– volume: 5
  start-page: 66
  year: 2024
  ident: 6980_CR72
  publication-title: Int J Cognit Comput Eng
  doi: 10.1016/j.ijcce.2023.12.002
– volume: 22
  year: 2020
  ident: 6980_CR78
  publication-title: J Med Internet Res
  doi: 10.2196/18585
– volume: 122
  start-page: 56
  year: 2020
  ident: 6980_CR42
  publication-title: J Clin Epidemiol
  doi: 10.1016/j.jclinepi.2020.03.002
– ident: 6980_CR15
  doi: 10.24432/C5G020
– volume: 9
  start-page: 350
  year: 2022
  ident: 6980_CR73
  publication-title: Bioengineering
  doi: 10.3390/bioengineering9080350
– volume: 52
  start-page: 4543
  year: 2022
  ident: 6980_CR81
  publication-title: Appl Intell
  doi: 10.1007/s10489-021-02550-9
– year: 2018
  ident: 6980_CR21
  publication-title: EAI Endorsed Trans Pervasive Health Technol
  doi: 10.4108/eai.13-8-2021.170671
– volume: 1
  start-page: 534
  year: 2023
  ident: 6980_CR25
  publication-title: Biomed Mater Devices
  doi: 10.1007/s44174-022-00027-y
– volume: 35
  start-page: 1953
  year: 2022
  ident: 6980_CR47
  publication-title: J Nephrol
  doi: 10.1007/S40620-022-01302-3/TABLES/9
– volume: 9
  start-page: 165184
  year: 2021
  ident: 6980_CR74
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3129491
– volume: 20
  start-page: 460
  issue: 7
  year: 2024
  ident: 6980_CR3
  publication-title: Nat Rev Nephrol
  doi: 10.1038/s41581-024-00828-y
– volume: 6
  year: 2023
  ident: 6980_CR82
  publication-title: Decis Anal J
  doi: 10.1016/j.dajour.2023.100169
– volume: 22
  start-page: 1
  year: 2021
  ident: 6980_CR45
  publication-title: BMC Nephrol
  doi: 10.1186/S12882-021-02474-Z/FIGURES/5
– volume: 20
  start-page: 473
  issue: 7
  year: 2024
  ident: 6980_CR5
  publication-title: Nat Rev Nephrol
  doi: 10.1038/s41581-024-00820-6
– volume: 24
  year: 2021
  ident: 6980_CR51
  publication-title: Inform Med Unlocked
  doi: 10.1016/j.imu.2021.100631
– volume: 42
  start-page: 268
  year: 2021
  ident: 6980_CR19
  publication-title: IRBM
  doi: 10.1016/J.IRBM.2020.07.002
– year: 2024
  ident: 6980_CR36
  publication-title: Int Urol Nephrol
  doi: 10.1007/S11255-024-04281-5/METRICS
– year: 2022
  ident: 6980_CR66
  publication-title: Int J Health Sci (Qassim)
  doi: 10.53730/ijhs.v6nS2.5579
– volume: 273
  start-page: 281
  year: 2022
  ident: 6980_CR80
  publication-title: Innov Nephrol Breakthr Technol Kidney Dis Care
  doi: 10.1007/978-3-031-11570-7_17
– volume: 9
  start-page: 546
  year: 2021
  ident: 6980_CR30
  publication-title: Healthcare
  doi: 10.3390/healthcare9050546
– volume: 16
  year: 2022
  ident: 6980_CR32
  publication-title: Intell Syst Appl
  doi: 10.1016/j.iswa.2022.200144
– volume: 15
  start-page: 817
  year: 2022
  ident: 6980_CR55
  publication-title: Risk Manag Healthc Policy
  doi: 10.2147/RMHP.S346856
– volume: 2
  start-page: e295
  year: 2020
  ident: 6980_CR20
  publication-title: Lancet Digit Health
  doi: 10.1016/S2589-7500(20)30063-7
– volume: 11
  start-page: 368
  year: 2019
  ident: 6980_CR33
  publication-title: Int J Med Eng Inform
  doi: 10.1504/IJMEI.2019.104981
– volume: 223
  year: 2023
  ident: 6980_CR53
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2023.119851
– volume: 82
  start-page: 217
  year: 2010
  ident: 6980_CR59
  publication-title: Scientometrics
  doi: 10.1007/s11192-009-0008-z
– ident: 6980_CR61
  doi: 10.1007/978-3-031-11570-7_26
– volume: 12
  start-page: 1
  issue: 1
  year: 2022
  ident: 6980_CR34
  publication-title: Sci Rep
  doi: 10.1038/s41598-022-26160-8
– volume: 82
  start-page: 41253
  year: 2023
  ident: 6980_CR49
  publication-title: Multimed Tools Appl
  doi: 10.1007/S11042-023-15188-1/TABLES/3
– volume: 12
  start-page: 302
  year: 2022
  ident: 6980_CR69
  publication-title: J Oral Biol Craniofac Res
  doi: 10.1016/j.jobcr.2021.11.010
– volume: 12
  start-page: e382
  year: 2024
  ident: 6980_CR7
  publication-title: Lancet Glob Health
  doi: 10.1016/S2214-109X(23)00570-3
– volume: 27
  start-page: 222
  year: 2024
  ident: 6980_CR63
  publication-title: Comput Methods Biomech Biomed Engin
  doi: 10.1080/10255842.2023.2181660
– volume: 348
  start-page: 457
  year: 2022
  ident: 6980_CR23
  publication-title: Lect Notes Netw Syst
  doi: 10.1007/978-981-16-7597-3_38
– volume: 1404
  start-page: 93
  year: 2019
  ident: 6980_CR16
  publication-title: Lect Notes Intell Transp Infrastruct Part F
  doi: 10.1007/978-3-030-01560-2_5
– volume: 11
  start-page: 138
  year: 2021
  ident: 6980_CR64
  publication-title: J Med Signals Sens
  doi: 10.4103/jmss.JMSS_13_20
– year: 2024
  ident: 6980_CR62
  publication-title: Multimed Tools Appl
  doi: 10.1007/s11042-024-20205-y
– volume: 14
  start-page: 1
  issue: 1
  year: 2024
  ident: 6980_CR67
  publication-title: Sci Rep
  doi: 10.1038/s41598-024-74993-2
– volume: 294
  start-page: 567
  year: 2020
  ident: 6980_CR9
  publication-title: Ann Oper Res
  doi: 10.1007/s10479-018-2818-y
– volume: 45
  start-page: 647
  year: 2023
  ident: 6980_CR39
  publication-title: Int J Comput Appl
  doi: 10.1080/1206212X.2023.2262786
– volume: 14
  start-page: 542
  year: 2023
  ident: 6980_CR60
  publication-title: Information
  doi: 10.3390/info14100542
– volume: 21
  start-page: 1
  year: 2020
  ident: 6980_CR54
  publication-title: BMC Nephrol
  doi: 10.1186/S12882-020-02151-7/FIGURES/4
– volume: 21
  start-page: 14011
  year: 2021
  ident: 6980_CR12
  publication-title: IEEE Sens J
  doi: 10.1109/JSEN.2020.3034904
– volume: 87
  start-page: 105368
  year: 2024
  ident: 6980_CR77
  publication-title: Biomed Signal Process Control
  doi: 10.1016/j.bspc.2023.105368
– volume: 19
  start-page: 229
  issue: 4
  year: 2023
  ident: 6980_CR6
  publication-title: Nat Rev Nephrol
  doi: 10.1038/s41581-022-00671-z
– volume: 16
  start-page: 51
  issue: 1
  year: 2019
  ident: 6980_CR11
  publication-title: Nat Rev Nephrol
  doi: 10.1038/s41581-019-0191-y
– volume: 31
  start-page: 179
  year: 2019
  ident: 6980_CR31
  publication-title: Lect Notes Comput Vis Biomech
  doi: 10.1007/978-3-030-04061-1_18
– volume: 22
  start-page: 2625
  year: 2022
  ident: 6980_CR65
  publication-title: Sensors
  doi: 10.3390/s22072625
– volume: 19
  start-page: 226
  year: 2021
  ident: 6980_CR75
  publication-title: Int J Environ Res Public Health
  doi: 10.3390/ijerph19010226
– year: 2024
  ident: 6980_CR83
  publication-title: Digit Health
  doi: 10.1177/20552076231224225
– volume: 20
  start-page: 21
  year: 2021
  ident: 6980_CR70
  publication-title: Comput Intell Neurosci
  doi: 10.1155/2021/2487759
– volume: 209
  year: 2021
  ident: 6980_CR40
  publication-title: Comput Methods Programs Biomed
  doi: 10.1016/j.cmpb.2021.106329
– volume: 35
  year: 2025
  ident: 6980_CR86
  publication-title: Int J Imaging Syst Technol
  doi: 10.1002/IMA.70016
– volume: 3
  start-page: 1
  year: 2019
  ident: 6980_CR17
  publication-title: IEEE Sens Lett
  doi: 10.1109/LSENS.2019.2942145
– volume: 95
  start-page: 3
  year: 2022
  ident: 6980_CR43
  publication-title: Lect Notes Data Eng Commun Technol
  doi: 10.1007/978-981-16-6636-0_1
– ident: 6980_CR28
– year: 2023
  ident: 6980_CR48
  publication-title: Comput Intell Neurosci
  doi: 10.1155/2023/9266889
– volume: 12
  start-page: 3673
  year: 2022
  ident: 6980_CR44
  publication-title: Appl Sci
  doi: 10.3390/app12073673
– volume: 40
  start-page: e12696
  year: 2024
  ident: 6980_CR57
  publication-title: Comput Intell
  doi: 10.1111/COIN.12696
– volume: 2020
  year: 2021
  ident: 6980_CR29
  publication-title: J Phys Conf Ser
  doi: 10.1088/1742-6596/2020/1/012047
– volume: 603
  start-page: 21
  year: 2022
  ident: 6980_CR46
  publication-title: Biochem Biophys Res Commun
  doi: 10.1016/j.bbrc.2022.02.099
– volume: 9
  start-page: 165132
  year: 2021
  ident: 6980_CR71
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3133700
SSID ssj0003307759
ssj0002793483
ssib051670015
Score 2.2922323
Snippet Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression...
Abstract Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of...
SourceID doaj
proquest
crossref
springer
SourceType Open Website
Aggregation Database
Index Database
Publisher
StartPage 528
SubjectTerms Abnormalities
Accuracy
Algorithms
Applications programs
Applied and Technical Physics
Automation
Biomarkers
Cardiovascular diseases
Chemistry/Food Science
Chronic kidney disease
Classification
Computing time
Datasets
Decision trees
Deep learning
Earth Sciences
End-stage renal disease
Engineering
Ensemble learning
Environment
Feature selection
Internet of medical things
IoMT
Kidney diseases
Kidneys
Learning algorithms
Machine learning
Materials Science
MKR stacking
MKR Voting
Mobile communications networks
Mobile computing
Multilayer perceptrons
Neural networks
Predictions
R&D
Real time
Renal function
Research & development
Robot learning
RPA
SummonAdditionalLinks – databaseName: Science Database
  dbid: M2P
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VlgMcyltsW5AP3MAifiSxT1VBVD2UqgdAvVmOH6uCull2U6T-e8aO0wcSXLg6DyWa8cw3nplvAN4grNdM6Ya2so5UshCoxeCHRiY8E63rbGbn_3bcnpyoszN9Wg7c1qWscrKJ2VD73qUz8veCp2CmFY3aX_6kaWpUyq6WERr3YAuRDUslXZ_56aRPNUs9KMXdfc9JNi1kZubkGH9RgcpX-mhyN11KAnKa5rtWjVYV1Xd8Vab0v4ND_0idZo90-Oh__-UxbBcsSg5G5XkCG2HxFB7eYih8BlcHZNV3_UCWY0MBsZdDP3Y7kuQAPbnoOzQs5FYinCAOJiERJ-NTKQ-UF_tI3EjES36c-0W4IiU3RFLp_Zxc5KrOQMoYi_lz-Hr46cvHI1qmNVAnFB-o1Dz4tnLcSR5ZbXmsvfTe-liHJui6kxgsOe2U9MynsCuyNmD0w52oIoJQ8QI2F_0ivASCEEMGzrxtlJWt9VZxLWPsFMIz9KZ2Bm8nuZjlSMphrumXsxQNStFkKRo9gw9JdNd3JkLtvNCv5qbsT-Nl6Gyrg-h8lGjFtRPouIW0QlgtKzeDvUmUpuzytbmR4wzeTcpwc_nvn7Tz77ftwgOe1bCmXOzB5rC6DK_gvvs1nK9Xr7OO_wY_2gBO
  priority: 102
  providerName: ProQuest
Title A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning
URI https://link.springer.com/article/10.1007/s42452-025-06980-9
https://www.proquest.com/docview/3208257368
https://doaj.org/article/d4eba79e3bdf42199c328434a33a940c
Volume 7
WOSCitedRecordID wos001493470000006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0003307759
  issn: 3004-9261
  databaseCode: DOA
  dateStart: 20240101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0003307759
  issn: 3004-9261
  databaseCode: M~E
  dateStart: 20240101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Earth, Atmospheric & Aquatic Science Database
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: PCBAR
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/eaasdb
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Engineering Database (subscription)
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: M7S
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Environmental Science Database (subscripiton)
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: PATMY
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/environmentalscience
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Materials Science Database
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: KB.
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/materialsscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central - New (Subscription)
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: BENPR
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: PIMPY
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Science Database (subscription)
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: M2P
  dateStart: 20190101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/sciencejournals
  providerName: ProQuest
– providerCode: PRVAVX
  databaseName: SpringerLink Open Access Journals
  customDbUrl:
  eissn: 3004-9261
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002793483
  issn: 3004-9261
  databaseCode: C24
  dateStart: 20210101
  isFulltext: true
  titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22
  providerName: Springer Nature
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PSxwxFA5FPfQiFS3dViUHbxo7k2QmyXFXVhR1Gawt9hQy-SEi7sg6Cl78233JzNq1UHrx8gZmMhDeS_Lex3v5HkI7ENarXKqSCF4EwnPviQHwQ0LOXM6ErU1i5_91KiYTeXmpqoVWX7EmrKMH7hT33XFfG6E8q13gsL2UZXCiMm4YM4pnNp6-mVALYCqewYDShShUf0sm3ZWLKT5KYvfWrFQyI-qNJ0qE_W-izL8So8nfHH5Cq32giIfdBNfQBz9dR09DPGvqpsV3XX0_Ng9t010-xNEfOXzb1LDP8UJeGkNYin3kMYa_YlomvWwCth0vLr65dlP_hPtUDY6V8Ff4NhVZetx3lbjaQD8PxxcHR6RvnkAsk7QlXFHvRGap5TTkhaGhcNw540LhS6-KmgN2scpK7nIXUVDIhQcwQi3LAsSE7DNamjZT_wVh8Pjc09yZUhoujDOSKh5CLSFaAudmBmh3rkh913Fk6Fc25KR2DWrXSe1aDdAo6vp1ZOS3Ti_A6rq3uv6f1Qdoc24p3W-6e81oxLuClXKA9ubW-_P531P6-h5T-oY-0rS6CkLZJlpqZw9-C63Yx_b6fraNlkfjSXW-nZYpyJPRPsgzWkUpfkT5PIZR1cFoeB6fx2fV7xeGnfEa
linkProvider Directory of Open Access Journals
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceCMWCvgAJ7CIH3n4gFB5VK26rHooqDfj-LEqqJtlNwXtn-I3MnaSPpDg1gNXJ7Hy-DzzTcbzDcAzpPWKVaqgpcwDlcx7ajD4oYEJx0Rpa5PU-T-Py8mkOjhQe2vwa6iFidsqB5uYDLVrbPxH_krwGMyUoqjezL_T2DUqZleHFhodLHb96ieGbMvXO-_x-z7nfOvD_rtt2ncVoFZUvKVSce_KzHIreWC54SF30jnjQu4Lr_JaIqm3ylbSMRfDg8BKjyydW5EFJEsC570El2VUFotbBfnegN-cxZqX3r1-TUk9JWRSAuUY71GBYO_rdlL1Xkw6chr7yWaFqjKqzvnG1ELgHO_9I1WbPODWzf_t3d2CGz3XJpvd4rgNa352B66fUWC8C6tNsmjqpiXzrmCCmOO26ao5SXTwjhw1NRpOcibRT5DnEx-FofGqmOdKg00gthMaJt8O3cyvSJ_7IrG0YEqO0q5VT_o2HdN78OlCnv0-rM-amX8ABCmU9Jw5U1RGlsaZiisZQl0h_US2YEbwYsCBnneiI_pEXjqhRiNqdEKNViN4G6FycmYUDE8DzWKqe_ujnfS1KZUXtQsSvZSyAomJkEYIo2RmR7AxQEf3VmypT3EzgpcD-E4P__2WHv57tqdwdXv_41iPdya7j-AaT0sgp1xswHq7OPaP4Yr90R4uF0_S-iLw5aJB-Rtr-F0J
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLUJw4I1YKOADnMBqYjsPHxAqlBWrltUeAJVTcPxYFdTNspuC9q_x6xg7Th9IcOuBq5NYeXye-Sbj-QbgKdJ6mZYyp4XIHBWptVRh8ENdyk3KC12roM7_ab-YTMqDAzndgF99LYzfVtnbxGCoTaP9P_JtznwwU_C83HZxW8R0d_Rq8Z36DlI-09q30-ggsmfXPzF8W70c7-K3fsbY6O2HN-9o7DBANS9ZS4Vk1hSJZlowl2aKucwIY5Rxmc2tzGqBBF9LXQqTGh8quLSwyNiZ5olD4sRx3kuwiZRcsAFsTsfvp597NGepr4CJzvZrSPFJLoIuKMPoj3KEfqziCbV8PgXJqO8um-SyTKg85ylDQ4FzLPiPxG3wh6Mb__ObvAnXIwsnO92yuQUbdn4brp3RZrwD6x2ybOqmJYuulIKo47bp6jyJd_2GHDU1mlRyZgsAwQiAWC8ZjVf5DFgYbBzRnQQx-XZo5nZNYlaM-KKDGTkK-1ktiQ08Znfh44U8-z0YzJu5vQ8EyZWwLDUqL5UolFElk8K5ukRiijxCDeF5j4lq0cmRVCfC0wFBFSKoCgiq5BBee9icnOmlxMNAs5xV0TJVRthaFdLy2jiB_ktqjpSFC8W5kiLRQ9jqYVRF-7aqTjE0hBc9EE8P__2WHvx7tidwBbFY7Y8new_hKgurIaOMb8GgXR7bR3BZ_2gPV8vHcbER-HLRqPwNKeJnUg
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+robot+process+automation+based+mobile+application+for+early+prediction+of+chronic+kidney+disease+using+machine+learning&rft.jtitle=Discover+applied+sciences&rft.au=Bijoy%2C+Md.+Hasan+Imam&rft.au=Mia%2C+Md.+Jueal&rft.au=Rahman%2C+Md.+Mahbubur&rft.au=Arefin%2C+Mohammad+Shamsul&rft.date=2025-05-23&rft.pub=Springer+International+Publishing&rft.eissn=3004-9261&rft.volume=7&rft.issue=6&rft_id=info:doi/10.1007%2Fs42452-025-06980-9&rft.externalDocID=10_1007_s42452_025_06980_9
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=3004-9261&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=3004-9261&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=3004-9261&client=summon