Early diagnosis of Parkinson’s disease using machine learning algorithms

Parkinson’s disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60–80%of these cells...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Medical hypotheses Jg. 138; S. 109603
1. Verfasser:	Karapinar Senturk, Zehra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.05.2020
Schlagworte:	Algorithms Decision support systems Early Diagnosis Feature selection Humans Machine Learning Medical diagnosis Parkinson Disease - diagnosis Support Vector Machine Support Vector Machines Decision support systems Feature selection Medical diagnosis Support Vector Machines Machine learning
ISSN:	0306-9877, 1532-2777, 1532-2777
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Parkinson’s disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60–80%of these cells are lost, then enough dopamine is not produced and Parkinson’s motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson’s disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson’s diagnosis.
AbstractList	Parkinson's disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60-80%of these cells are lost, then enough dopamine is not produced and Parkinson's motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson's disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson's diagnosis.Parkinson's disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60-80%of these cells are lost, then enough dopamine is not produced and Parkinson's motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson's disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson's diagnosis. Parkinson’s disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60–80%of these cells are lost, then enough dopamine is not produced and Parkinson’s motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson’s disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson’s diagnosis.
ArticleNumber	109603
Author	Karapinar Senturk, Zehra
Author_xml	– sequence: 1 givenname: Zehra surname: Karapinar Senturk fullname: Karapinar Senturk, Zehra email: zehrakarapinar@duzce.edu.tr organization: Duzce University, Engineering Faculty, Department of Computer Engineering, 81620 Duzce, Turkey
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32028195$$D View this record in MEDLINE/PubMed
BookMark	eNqFkM9u1DAQhy1URLeFF-CAcuSSxY6T2EZcUFX-qRIc4GxNnMnubB272FmkvfEavF6fpIm2vfRQTjOa-X0jzXfGTkIMyNhrwdeCi_bdbj3i9rCueLUMTMvlM7YSjazKSil1wlZc8rY0WqlTdpbzjnNuaqlfsFM5M1qYZsW-XULyh6In2ISYKRdxKH5AuqaQY7j9-y_Pq4yQsdhnCptiBLelgIVHSGEZgN_ERNN2zC_Z8wF8xlf39Zz9-nT58-JLefX989eLj1elqxsxlabvBxRatboyAjow6JSDuoVGAmgjO-N6rlFjJxsphk40Blw9dw0Kp7SS5-zt8e5Nir_3mCc7UnboPQSM-2wr2VRtzZUyc_TNfXTfjdjbm0QjpIN9-H8O6GPApZhzwsE6mmCiGKYE5K3gdlFtd3ZRbRfV9qh6RqtH6MP1J6EPRwhnQX8Ik82OMDjsKaGbbB_pafz9I9x5CuTAX-Phf_AdbWmsIA
CitedBy_id	crossref_primary_10_1186_s13550_025_01245_3 crossref_primary_10_1007_s10772_021_09837_9 crossref_primary_10_1007_s10072_024_07956_0 crossref_primary_10_1007_s11042_025_20599_3 crossref_primary_10_1016_j_jclepro_2020_125324 crossref_primary_10_1109_ACCESS_2025_3575023 crossref_primary_10_3390_biomedicines13071764 crossref_primary_10_1002_wjo2_70017 crossref_primary_10_1109_ACCESS_2024_3405009 crossref_primary_10_3390_electronics12102290 crossref_primary_10_1109_ACCESS_2024_3520482 crossref_primary_10_1016_j_procs_2025_04_132 crossref_primary_10_1142_S0219467825500639 crossref_primary_10_3390_bioengineering11010088 crossref_primary_10_1007_s12144_024_06891_9 crossref_primary_10_3390_s23115243 crossref_primary_10_1109_TAI_2022_3193651 crossref_primary_10_3390_diagnostics12071543 crossref_primary_10_1007_s12553_021_00555_5 crossref_primary_10_1007_s11042_024_18186_z crossref_primary_10_1007_s13312_021_2228_0 crossref_primary_10_7717_peerj_cs_1702 crossref_primary_10_1007_s13534_023_00319_2 crossref_primary_10_1038_s41598_022_24541_7 crossref_primary_10_1007_s11831_022_09710_1 crossref_primary_10_3390_diagnostics12102404 crossref_primary_10_1155_2023_1493676 crossref_primary_10_1007_s11042_023_14647_z crossref_primary_10_3389_fgene_2023_1230579 crossref_primary_10_1007_s11042_025_20904_0 crossref_primary_10_3390_ijms242216119 crossref_primary_10_1109_ACCESS_2025_3572092 crossref_primary_10_17163_ings_n25_2021_02 crossref_primary_10_3390_math8091620 crossref_primary_10_1007_s42979_023_02313_y crossref_primary_10_1111_exsy_13790 crossref_primary_10_2196_69422 crossref_primary_10_56294_sctconf20251353 crossref_primary_10_1007_s44174_025_00279_4 crossref_primary_10_1007_s10772_023_10068_3 crossref_primary_10_1109_ACCESS_2025_3600685 crossref_primary_10_32604_cmc_2024_048967 crossref_primary_10_3389_fneur_2022_831428 crossref_primary_10_3390_diagnostics12082003 crossref_primary_10_1007_s10772_024_10152_2 crossref_primary_10_1007_s11042_024_18398_3 crossref_primary_10_1111_exsy_13569 crossref_primary_10_1007_s42979_025_04201_z crossref_primary_10_3390_s23218936 crossref_primary_10_3390_jcdd9080268 crossref_primary_10_1016_j_cmpb_2023_107495 crossref_primary_10_3390_diagnostics10060421 crossref_primary_10_1016_j_ecoinf_2022_101640 crossref_primary_10_1080_03772063_2024_2434572 crossref_primary_10_1007_s13369_025_10148_3 crossref_primary_10_1007_s11042_023_16156_5 crossref_primary_10_1002_hbm_25838 crossref_primary_10_3233_JIFS_230183 crossref_primary_10_1007_s42979_024_03187_4 crossref_primary_10_1016_j_matpr_2020_10_063 crossref_primary_10_1177_20552076251352941 crossref_primary_10_1007_s11277_023_10285_8 crossref_primary_10_1016_j_bspc_2023_104870 crossref_primary_10_3390_math12101575 crossref_primary_10_3233_JPD_230002 crossref_primary_10_3390_diagnostics13111924 crossref_primary_10_1016_j_jvoice_2024_04_020 crossref_primary_10_1007_s10462_025_11347_y crossref_primary_10_1002_eng2_13091 crossref_primary_10_1016_j_amjcard_2020_07_048 crossref_primary_10_3390_ijtm1030016 crossref_primary_10_1186_s42269_023_01047_4 crossref_primary_10_1007_s12144_022_02949_8 crossref_primary_10_1016_j_eswa_2022_117483 crossref_primary_10_1515_bmt_2022_0022 crossref_primary_10_1007_s00521_021_06626_y crossref_primary_10_1590_1678_4324_2025230860 crossref_primary_10_1007_s00521_024_09516_1 crossref_primary_10_1155_2022_6159392 crossref_primary_10_3390_app13179502 crossref_primary_10_1186_s12880_025_01677_2 crossref_primary_10_21015_vtse_v11i3_1598 crossref_primary_10_3390_w15193487 crossref_primary_10_1109_ACCESS_2021_3108892 crossref_primary_10_7717_peerj_cs_2031 crossref_primary_10_1016_j_energy_2020_119076 crossref_primary_10_1080_10255842_2025_2542942 crossref_primary_10_1016_j_artmed_2025_103109 crossref_primary_10_3389_fnagi_2021_633752 crossref_primary_10_1515_bams_2021_0064 crossref_primary_10_1109_ACCESS_2021_3062484 crossref_primary_10_1007_s42044_025_00232_0 crossref_primary_10_1155_2022_2793361 crossref_primary_10_3390_diagnostics12010166 crossref_primary_10_1186_s12911_022_01909_3 crossref_primary_10_1016_j_patrec_2023_03_011 crossref_primary_10_1080_0954898X_2025_2457955 crossref_primary_10_1016_j_compbiomed_2024_107959 crossref_primary_10_3389_frai_2023_1084001 crossref_primary_10_1007_s11042_024_20108_y crossref_primary_10_1007_s40883_022_00273_y crossref_primary_10_4236_jcc_2025_138014 crossref_primary_10_3390_bioengineering9070283 crossref_primary_10_3390_biomedinformatics5020023 crossref_primary_10_1007_s40846_023_00796_x crossref_primary_10_1007_s11517_023_02803_4 crossref_primary_10_1007_s00521_021_05974_z crossref_primary_10_3390_computation12110230 crossref_primary_10_1177_18724981251353596 crossref_primary_10_1007_s10278_024_01316_2 crossref_primary_10_3390_ai6040068 crossref_primary_10_1007_s10439_023_03303_0 crossref_primary_10_1007_s00521_024_09596_z crossref_primary_10_1109_ACCESS_2024_3487001 crossref_primary_10_3390_bioengineering9030116
Cites_doi	10.1016/j.bspc.2016.08.003 10.1007/s10916-009-9272-y 10.1016/j.future.2018.02.009 10.1016/j.compbiomed.2019.103375 10.1016/j.compbiomed.2019.103347 10.1016/j.bbe.2017.09.002 10.1016/j.procs.2019.06.052 10.1016/j.clinph.2018.09.018 10.1016/j.procs.2018.10.031 10.1016/j.eij.2018.03.002 10.1080/00207721.2011.581395 10.1016/j.ifacol.2016.07.331 10.1016/j.eswa.2009.06.040 10.1016/j.neucom.2018.04.049 10.1016/j.parkreldis.2019.02.028 10.1016/j.patrec.2019.04.005 10.1016/j.eswa.2018.06.003 10.1016/j.bspc.2017.06.015
ContentType	Journal Article
Copyright	2020 Elsevier Ltd Copyright © 2020 Elsevier Ltd. All rights reserved.
Copyright_xml	– notice: 2020 Elsevier Ltd – notice: Copyright © 2020 Elsevier Ltd. All rights reserved.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1016/j.mehy.2020.109603
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	1532-2777
ExternalDocumentID	32028195 10_1016_j_mehy_2020_109603 S0306987719314148
Genre	Journal Article
GroupedDBID	--- --K --M .1- .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29M 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAWTL AAXKI AAXUO AAYWO ABBQC ABFNM ABGSF ABJNI ABLJU ABMAC ABMZM ABUDA ABWVN ABXDB ABZDS ACDAQ ACGFS ACIEU ACLOT ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO ADUVX AEBSH AEHWI AEIPS AEKER AENEX AEUPX AEVXI AFFNX AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGRDE AGUBO AGYEJ AHHHB AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALCLG ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CAG COF CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HEA HMK HMO HVGLF HZ~ IHE J1W KOM LZ2 M29 M41 MO0 N9A O-L O9- OAUVE OGGZJ OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SCC SDF SDG SDP SEL SES SEW SPCBC SSH SSP SSU SSZ T5K UHS WUQ Z5R ZGI ~G- ~HD AACTN AAIAV AATCM ABLVK ABYKQ AFCTW AFKWA AJBFU AJOXV AMFUW DOVZS LCYCR RIG 9DU AAYXX CITATION AGCQF AGRNS CGR CUY CVF ECM EIF NPM 7X8
ID	FETCH-LOGICAL-c451t-9ddfe18768291aba9ec7ca46a53aa893b9cd08e8eb3531fb159ac431f5e1c7873
ISICitedReferencesCount	157
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000523642300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0306-9877 1532-2777
IngestDate	Sun Nov 09 09:59:55 EST 2025 Mon Jul 21 05:59:28 EDT 2025 Tue Nov 18 21:56:18 EST 2025 Sat Nov 29 07:19:53 EST 2025 Fri Feb 23 02:48:48 EST 2024 Tue Oct 14 19:25:11 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Decision support systems Feature selection Medical diagnosis Support Vector Machines Machine learning
Language	English
License	Copyright © 2020 Elsevier Ltd. All rights reserved.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c451t-9ddfe18768291aba9ec7ca46a53aa893b9cd08e8eb3531fb159ac431f5e1c7873
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
OpenAccessLink	https://hdl.handle.net/20.500.12684/5647
PMID	32028195
PQID	2352640779
PQPubID	23479
ParticipantIDs	proquest_miscellaneous_2352640779 pubmed_primary_32028195 crossref_citationtrail_10_1016_j_mehy_2020_109603 crossref_primary_10_1016_j_mehy_2020_109603 elsevier_sciencedirect_doi_10_1016_j_mehy_2020_109603 elsevier_clinicalkey_doi_10_1016_j_mehy_2020_109603
PublicationCentury	2000
PublicationDate	May 2020 2020-05-00 2020-May 20200501
PublicationDateYYYYMMDD	2020-05-01
PublicationDate_xml	– month: 05 year: 2020 text: May 2020
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Medical hypotheses
PublicationTitleAlternate	Med Hypotheses
PublicationYear	2020
Publisher	Elsevier Ltd
Publisher_xml	– name: Elsevier Ltd
References	Öztemel (b0120) 2012 Karabudak (b0005) 2014 Sakar, Kursun (b0010) 2010; 34 Zhu, Zeng, Wang (b0125) 2010 Rayan, Alfonse, Salem (bib126) 2019; 154 Das (b0055) 2010; 37 Prashanth, Dutta Roy (b0030) 2018; 305 Yaman, Ertam, Tuncer (b0080) 2019 UCI machine learning repository: Parkinsons data set. [Online]. Available Cavallo, Moschetti, Esposito, Maremmani, Rovini (b0035) 2019; 63 Almeida (b0040) 2019; 125 Wan, Maszczyk, See, Dauwels, King (b0015) 2019; 130 Kotsavasiloglou, Kostikis, Hristu-Varsakelis, Arnaoutoglou (b0085) 2017; 31 Jain, Singh (b0100) 2018; 19 Polat (b0060) 2012; 43 Wang, Wang, Ai, Sun (b0065) 2017; 38 Singh, Vadera, Samavedham, Lim (b0070) 2016; 49 Abdulhay, Arunkumar, Narasimhan, Vellaiappan, Venkatraman (b0075) 2018; 83 Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Salmanpour (b0020) 2019; 111 Nilashi, Ibrahim, Ahmadi, Shahmoradi, Farahmand (b0050) 2018; 38 Remeseiro, Bolon-Canedo (b0095) 2019; 112 Pedrosa, Vasconcelos, Medeiros, Silva (b0025) 2018; 138 [Accessed: 10-Dec-2019]. Yohannes, Webb (b0110) 1999 Hsu, Chang, Lin (b0115) 2003 Parisi, RaviChandran, Manaog (b0045) 2018; 110 Salmanpour (10.1016/j.mehy.2020.109603_b0020) 2019; 111 Rayan (10.1016/j.mehy.2020.109603_bib126) 2019; 154 Wan (10.1016/j.mehy.2020.109603_b0015) 2019; 130 Polat (10.1016/j.mehy.2020.109603_b0060) 2012; 43 Zhu (10.1016/j.mehy.2020.109603_b0125) 2010 Nilashi (10.1016/j.mehy.2020.109603_b0050) 2018; 38 Öztemel (10.1016/j.mehy.2020.109603_b0120) 2012 Yaman (10.1016/j.mehy.2020.109603_b0080) 2019 Wang (10.1016/j.mehy.2020.109603_b0065) 2017; 38 10.1016/j.mehy.2020.109603_b0090 Prashanth (10.1016/j.mehy.2020.109603_b0030) 2018; 305 Abdulhay (10.1016/j.mehy.2020.109603_b0075) 2018; 83 Sakar (10.1016/j.mehy.2020.109603_b0010) 2010; 34 Kotsavasiloglou (10.1016/j.mehy.2020.109603_b0085) 2017; 31 10.1016/j.mehy.2020.109603_b0105 Hsu (10.1016/j.mehy.2020.109603_b0115) 2003 Singh (10.1016/j.mehy.2020.109603_b0070) 2016; 49 Cavallo (10.1016/j.mehy.2020.109603_b0035) 2019; 63 Remeseiro (10.1016/j.mehy.2020.109603_b0095) 2019; 112 Yohannes (10.1016/j.mehy.2020.109603_b0110) 1999 Das (10.1016/j.mehy.2020.109603_b0055) 2010; 37 Karabudak (10.1016/j.mehy.2020.109603_b0005) 2014 Pedrosa (10.1016/j.mehy.2020.109603_b0025) 2018; 138 Almeida (10.1016/j.mehy.2020.109603_b0040) 2019; 125 Parisi (10.1016/j.mehy.2020.109603_b0045) 2018; 110 Jain (10.1016/j.mehy.2020.109603_b0100) 2018; 19
References_xml	– volume: 63 start-page: 111 year: 2019 end-page: 116 ident: b0035 article-title: Upper limb motor pre-clinical assessment in Parkinson’s disease using machine learning publication-title: Park Relat Disord – volume: 125 start-page: 55 year: 2019 end-page: 62 ident: b0040 article-title: Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques publication-title: Pattern Recogn Lett – volume: 49 start-page: 990 year: 2016 end-page: 995 ident: b0070 article-title: Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: a study on Parkinson’s Disease publication-title: IFAC-Papers OnLine – volume: 83 start-page: 366 year: 2018 end-page: 373 ident: b0075 article-title: Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease publication-title: Fut Gener Comput Syst – volume: 31 start-page: 174 year: 2017 end-page: 180 ident: b0085 article-title: Machine learning-based classification of simple drawing movements in Parkinson’s disease publication-title: Biomed Signal Process Control – volume: 130 start-page: 145 year: 2019 end-page: 154 ident: b0015 article-title: A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease publication-title: Clin Neurophysiol – volume: 138 start-page: 215 year: 2018 end-page: 220 ident: b0025 article-title: Machine learning application to quantify the tremor level for Parkinson’s disease patients publication-title: Procedia Comput Sci – volume: 305 start-page: 78 year: 2018 end-page: 103 ident: b0030 article-title: Novel and improved stage estimation in Parkinson’s disease using clinical scales and machine learning publication-title: Neurocomputing – reference: UCI machine learning repository: Parkinsons data set. [Online]. Available: – volume: 19 start-page: 179 year: 2018 end-page: 189 ident: b0100 article-title: Feature selection and classification systems for chronic disease prediction: a review publication-title: Egypt Inform J – year: 2014 ident: b0005 article-title: Parkinson’s disease – reference: Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. – reference: . [Accessed: 10-Dec-2019]. – volume: 112 year: 2019 ident: b0095 article-title: A review of feature selection methods in medical applications publication-title: Comput Biol Med – volume: 37 start-page: 1568 year: 2010 end-page: 1572 ident: b0055 article-title: A comparison of multiple classification methods for diagnosis of Parkinson disease publication-title: Expert Syst Appl – year: 1999 ident: b0110 article-title: Classification and Regression Trees, CART: a user manual for identifying indicators of vulnerability to famine and chronic food insecurity – volume: 38 start-page: 1 year: 2018 end-page: 15 ident: b0050 article-title: A hybrid intelligent system for the prediction of Parkinson’s Disease progression using machine learning techniques publication-title: Biocybern Biomed Eng – start-page: 109483 year: 2019 ident: b0080 article-title: Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features publication-title: Med Hypotheses – volume: 110 start-page: 182 year: 2018 end-page: 190 ident: b0045 article-title: Feature-driven machine learning to improve early diagnosis of Parkinson’s disease publication-title: Expert Syst Appl – year: 2003 ident: b0115 article-title: A practical guide to support vector classification – year: 2010 ident: b0125 article-title: Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS ® implementations – year: 2012 ident: b0120 article-title: Yapay Sinir Ağları – volume: 43 start-page: 597 year: 2012 end-page: 609 ident: b0060 article-title: Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering publication-title: Int J Syst Sci – volume: 111 year: 2019 ident: b0020 article-title: Optimized machine learning methods for prediction of cognitive outcome in Parkinson’s disease publication-title: Comput Biol Med – volume: 154 start-page: 361 year: 2019 end-page: 368 ident: bib126 article-title: Machine learning approaches in smart health publication-title: Procedia Comput Sci – volume: 38 start-page: 400 year: 2017 end-page: 410 ident: b0065 article-title: An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson’s disease publication-title: Biomed Signal Process Control – volume: 34 start-page: 591 year: 2010 end-page: 599 ident: b0010 article-title: Telediagnosis of Parkinson’s disease using measurements of dysphonia publication-title: J Med Syst – volume: 31 start-page: 174 year: 2017 ident: 10.1016/j.mehy.2020.109603_b0085 article-title: Machine learning-based classification of simple drawing movements in Parkinson’s disease publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2016.08.003 – volume: 34 start-page: 591 issue: 4 year: 2010 ident: 10.1016/j.mehy.2020.109603_b0010 article-title: Telediagnosis of Parkinson’s disease using measurements of dysphonia publication-title: J Med Syst doi: 10.1007/s10916-009-9272-y – volume: 83 start-page: 366 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0075 article-title: Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease publication-title: Fut Gener Comput Syst doi: 10.1016/j.future.2018.02.009 – volume: 112 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0095 article-title: A review of feature selection methods in medical applications publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.103375 – volume: 111 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0020 article-title: Optimized machine learning methods for prediction of cognitive outcome in Parkinson’s disease publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.103347 – volume: 38 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0050 article-title: A hybrid intelligent system for the prediction of Parkinson’s Disease progression using machine learning techniques publication-title: Biocybern Biomed Eng doi: 10.1016/j.bbe.2017.09.002 – volume: 154 start-page: 361 year: 2019 ident: 10.1016/j.mehy.2020.109603_bib126 article-title: Machine learning approaches in smart health publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2019.06.052 – volume: 130 start-page: 145 issue: 1 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0015 article-title: A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease publication-title: Clin Neurophysiol doi: 10.1016/j.clinph.2018.09.018 – volume: 138 start-page: 215 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0025 article-title: Machine learning application to quantify the tremor level for Parkinson’s disease patients publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2018.10.031 – volume: 19 start-page: 179 issue: 3 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0100 article-title: Feature selection and classification systems for chronic disease prediction: a review publication-title: Egypt Inform J doi: 10.1016/j.eij.2018.03.002 – start-page: 109483 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0080 article-title: Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features publication-title: Med Hypotheses – ident: 10.1016/j.mehy.2020.109603_b0090 – year: 2010 ident: 10.1016/j.mehy.2020.109603_b0125 – volume: 43 start-page: 597 issue: 4 year: 2012 ident: 10.1016/j.mehy.2020.109603_b0060 article-title: Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering publication-title: Int J Syst Sci doi: 10.1080/00207721.2011.581395 – ident: 10.1016/j.mehy.2020.109603_b0105 – volume: 49 start-page: 990 issue: 7 year: 2016 ident: 10.1016/j.mehy.2020.109603_b0070 article-title: Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: a study on Parkinson’s Disease publication-title: IFAC-Papers OnLine doi: 10.1016/j.ifacol.2016.07.331 – year: 2012 ident: 10.1016/j.mehy.2020.109603_b0120 – volume: 37 start-page: 1568 issue: 2 year: 2010 ident: 10.1016/j.mehy.2020.109603_b0055 article-title: A comparison of multiple classification methods for diagnosis of Parkinson disease publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2009.06.040 – volume: 305 start-page: 78 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0030 article-title: Novel and improved stage estimation in Parkinson’s disease using clinical scales and machine learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.04.049 – volume: 63 start-page: 111 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0035 article-title: Upper limb motor pre-clinical assessment in Parkinson’s disease using machine learning publication-title: Park Relat Disord doi: 10.1016/j.parkreldis.2019.02.028 – year: 1999 ident: 10.1016/j.mehy.2020.109603_b0110 – volume: 125 start-page: 55 year: 2019 ident: 10.1016/j.mehy.2020.109603_b0040 article-title: Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2019.04.005 – volume: 110 start-page: 182 year: 2018 ident: 10.1016/j.mehy.2020.109603_b0045 article-title: Feature-driven machine learning to improve early diagnosis of Parkinson’s disease publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.06.003 – year: 2003 ident: 10.1016/j.mehy.2020.109603_b0115 – year: 2014 ident: 10.1016/j.mehy.2020.109603_b0005 – volume: 38 start-page: 400 year: 2017 ident: 10.1016/j.mehy.2020.109603_b0065 article-title: An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson’s disease publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2017.06.015
SSID	ssj0009438
Score	2.653803
Snippet	Parkinson’s disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine.... Parkinson's disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine....
SourceID	proquest pubmed crossref elsevier
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	109603
SubjectTerms	Algorithms Decision support systems Early Diagnosis Feature selection Humans Machine Learning Medical diagnosis Parkinson Disease - diagnosis Support Vector Machine Support Vector Machines
Title	Early diagnosis of Parkinson’s disease using machine learning algorithms
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S0306987719314148 https://dx.doi.org/10.1016/j.mehy.2020.109603 https://www.ncbi.nlm.nih.gov/pubmed/32028195 https://www.proquest.com/docview/2352640779
Volume	138
WOSCitedRecordID	wos000523642300001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1532-2777 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009438 issn: 0306-9877 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwELVoixAXxDdboDISt1VWm9jZ2McKFUGBikORVlyiseN0W7XZVbKL2lv_Bn-PX8I4thOo2EIPXKIoikdOXvI8tufNEPJ6UqiyEKAjyZWOOJcqUnoCEUsKkZQ2ESprhcIfs4MDMZ3Kz76UXNOWE8iqSpyfy8V_hRqvIdhWOnsDuDujeAHPEXQ8Iux4_CfgXcriwoXQuWwjVtrsVF4-tEE2YWdmuGoXC87amEoTikgcDeH0aF4fL2c-mXmo-eS3dWYXC6vcavoAxA9Qw8Jqe5F97DDWkuxXM6vh13WFZNxH8QU91XgSSeGLrASudKlYPNvFdv7D_kjEbk3gZHRmZhcja37U3_x71usro1EXIxjCz05yayO3NnJnY4NsJVkqkcO2dt_vTff7JMu8rVze9dyLpFw839WerHNE1k00Wofj8D6552cKdNch_IDcMtVDcueTj4V4RPZboGkHNJ2XtAP6x-X3hnqIaQsx9RDTADHtIX5MvrzdO3zzLvKVMSLN03gZyaIoTYwDmUhkDAqk0ZkGPoGUAaAHqqQuxsIIoxhybKnQZwWNrmKZmlgjRbMnZLOaV-YZoabgKVMskRzGXBWZALQLaRlDloAo2YDE4TXl2qeNt9VLTvP1AA3IsGuzcElTrr2bhbefBzkwDmA5fkrXtkq7Vt5ZdE7gX9u9CgDnyKR2ewwqM181eWJLRfBxlskBeeqQ73rP0ILdcd6-0ZM9J3f7P-sF2VzWK_OS3NbflsdNvUM2sqnY8d_wT978n-4
linkProvider	Elsevier
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=Early+diagnosis+of+Parkinson%E2%80%99s+disease+using+machine+learning+algorithms&rft.jtitle=Medical+hypotheses&rft.au=Karapinar+Senturk%2C+Zehra&rft.date=2020-05-01&rft.issn=0306-9877&rft.volume=138&rft.spage=109603&rft_id=info:doi/10.1016%2Fj.mehy.2020.109603&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_mehy_2020_109603
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-9877&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-9877&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-9877&client=summon