Chronic Kidney Disease (CKD) Prediction using Machine Learning Algorithms on Patient Data from a Tertiary Hospital in the Philippines

Abstract

Chronic kidney disease (CKD) is a continuous decrease in kidney function and is a significant public health concern due to challenges with diagnostics. Artificial intelligence (AI) and machine learning methods have been applied in the medical field, particularly in disease prediction which has improved healthcare outcomes for patients worldwide. This study then aims to evaluate the performance of various machine learning classifiers for predicting CKD by analyzing patient data from a tertiary hospital in Metro Manila. Two publicly-available online databases from India and Bangladesh were combined, resulting in 600 instances of patient data with 14 features. Model training was then conducted using five different algorithms, namely (1) k-nearest neighbor, (2) logistic regression (L2 & L1), (3) support vector machine (L2 & L1), (4) random forest, and (5) gradient boosting methods. Validation was then performed using 200 instances of patient data from the tertiary hospital. Results of the study show that all trained models were fairly accurate (>80% accuracy) in predicting the occurrence of CKD in the tertiary hospital patient data. More specifically, linear SVM (L1) was the most accurate (85.5%), closely followed by linear SVM (L2) (84.5%). Hemoglobin was also found to be the top predictor for CKD. In conclusion, machine learning is an effective tool for binary classification tasks such as the prediction of disease occurrence.