Our findings, as illustrated in Fig. 3, demonstrate that Datasets_1 exhibited the best performance, utilizing a filter method based on correlation analysis for feature selection and identifying crucial predictors like age, blood pressure, specific gravity, albumin, sugar, blood glucose random, blood urea, serum creatinine, sodium, potassium, hemoglobin, white blood cell count, red blood cells, pus cell, pus cell clumps, bacteria, hypertension, diabetes mellitus, coronary artery disease, appetite, pedal edema, anemia. Here, ALB is linked to coronary artery disorder.