HSCDT: HYBRID SPECTRAL CLUSTERING DECISION TREE FOR PREDICTING CHRONIC KIDNEY DISEASE IN HEART DISEASE PATIENTS

Chronic Kidney Disease (CKD) is intertwined with cardiovascular disease (CVD), sharing common risk factors and underlying pathophysiologic processes. However, the early diagnosis of CKD in patients with cardiovascular diseases is crucial for timely intervention and improved patient outcomes. This research introduces a new Hybrid Spectral Clustering Decision Tree (HSCDT) model for predicting CKD in patients with cardiac disease. The method implements spectral clustering and applied decision tree classification for effective discovery of complex patterns in the data set. Various classifiers, including Gradient Boosting, AdaBoost, XGBoost, LightGBM, and Voting Classifier were selected for comparative analysis. The proposed model achieved a peak of 99% cross-validation ac-curacy and a test accuracy of 99%, outperforming previous classifiers. Important clinical predictors of chronic kidney disease (CKD), such as age, body mass index, smoking, history of coronary heart disease, blood pressure, cholesterol, serum creatinine, hypertension, and eGFR, were analyzed and included in the prediction model. The performance on the experimental data confirmed the model’s ability to accurately distinguish the CKD patients from the rest of the population. The findings suggest a significant enhancement in classification effectiveness when spectral clustering and decision trees are consolidated and provide this classification as a possible mode of disease prediction of CKD in the patient suffering from heart disease. Further research can focus on increasing datasets, involving additional biomarkers, and improving models for increased clinical utility.