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...

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Published in:Discover applied sciences Vol. 7; no. 6; pp. 528 - 34
Main Authors: Bijoy, Md. Hasan Imam, Mia, Md. Jueal, Rahman, Md. Mahbubur, Arefin, Mohammad Shamsul, Dhar, Pranab Kumar, Shimamura, Tetsuya
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 23.05.2025
Springer Nature B.V
Springer
Subjects:
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
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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 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.
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
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  organization: Faculty of Science and Engineering, Waseda University, Department of Information and Computer Sciences, Saitama University
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Chronic kidney disease
MKR Voting
MKR stacking
Machine learning
IoMT
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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...
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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
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Title A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning
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