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Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination
Ebrahime Mohammed Senan1, Mosleh Hmoud Al-Adhaileh2, Fawaz Waselallah Alsaade3
1Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India.
Journal of Healthcare Engineering
|July 2, 2021
Summary
Early diagnosis of chronic kidney disease (CKD) is crucial. Machine learning models, particularly random forest, achieved 100% accuracy in detecting CKD, aiding preventive measures.
Area of Science:
- Nephrology
- Artificial Intelligence
- Data Science
Background:
- Chronic kidney disease (CKD) affects 10% of adults globally and is a leading cause of death.
- Effective early diagnosis measures for CKD are essential due to its increasing prevalence.
- Current diagnostic methods require enhancement for timely intervention and prevention of kidney failure.
Purpose of the Study:
- To develop a machine learning-based diagnostic system for early detection of chronic kidney disease (CKD).
- To evaluate the performance of various machine learning algorithms in identifying CKD.
- To assist healthcare professionals in implementing preventive strategies through early CKD diagnosis.
Main Methods:
- A dataset of 400 patients with 24 features was analyzed.
- Missing numerical and nominal values were imputed using mean and mode imputation.
- Recursive Feature Elimination (RFE) was employed for feature selection.
- Four classification algorithms were applied: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Random Forest.
Main Results:
- All applied classification algorithms demonstrated promising performance in CKD detection.
- The Random Forest algorithm achieved superior results, reaching 100% accuracy, precision, recall, and F1-score.
- Feature selection using RFE identified key indicators for CKD diagnosis.
Conclusions:
- Machine learning techniques, especially Random Forest, are highly effective for the early detection of CKD.
- AI-powered diagnostic tools can significantly support clinicians in diagnosing CKD and preventing its progression.
- Early diagnosis through advanced computational methods is vital for managing CKD and reducing mortality rates.
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