Machine Learning Approach to Identify Spread of Kidney Diseases in Sri Lanka

chronic kidney disease (CKD) is a major public health problem in Sri Lanka, with an estimated prevalence of 10%. Early detection and intervention are essential for preventing progression to end-stage renal disease (ESRD), which requires dialysis or kidney transplantation. Machine learning (ML) models have been shown to be effective in predicting CKD in other countries. However, there is limited research on the use of ML models for CKD prediction in Sri Lanka. Data from Decision Trees, SVMs, Random Forest, and other machine learning algorithms were used, as well as 400 patient samples from CKD patients. In this current research project work, we aim to build efficient tools for predicting CKD occurrence, and the research contains 4 sub-parts, more specifically, first included predicting the CKD and suggested Ayurvedic treatments, second, we forecast the future medicine usage in hospitals, third, we used symptoms details to predict the patient's CKD and last predicted the diet plan for patients. The results of this study will provide valuable insights into the use of ML models for CKD prediction. The findings could be used to develop a national CKD screening program and to improve the early detection and control of CKD in the country.