Chronic Kidney Disease Prediction using Light Gradient Boosting Machine

Chronic Kidney Disease (CKD) is a condition categorized by progressive loss of kidney function throughout a period of time. Due to the loss of kidney function in the human body, excess fluid and waste from blood remains in the body and may lead to other health problems. Inadequate diagnosis and delayed treatment can ultimately result in end-stage renal disease. Machine Learning techniques have attained an important role in disease prediction used to forecast chronic kidney disease. Light Gradient Boosting Machine (LGBM) is the machine learning technique used for the prediction of chronic kidney disease at early stages in the human body. This research proposes a predictive model based on the LightGBM, a high-performance machine learning algorithm well-suited for large and complex medical datasets. The proposed model is designed to identify at risk of developing CKD at an early stage. This methodology involves data, feature selection, model training, and preprocessing with LGBM.