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HCB: A Hybrid CatBoost Enabled Chronic Kidney Disease Detection System

Chronic kidney disease (CKD) acts as one of the commonly occurring problem that causes death globally. The increasing scale of CKD patients created the need for finding the early prediction systems and solutions. Due to large dataset and recorded information of patients, the processing time is high and scaling the dataset in the initial stage is highlighted. The presented study is focused on the foremost problem of kidney disease prediction in the early stages using a robust machine learning algorithm. The proposed work is modeled with Hybrid CatBoost algorithm in which the iterative learning process enables the system to adjust the weight of the training pattern; hence an effective prediction is made. The system is used to make the initial correlation factor between the training vector and testing vector of the CKD patient’s data. Higher the learning rate, better the prediction accuracy. The proposed system is validated through a number of quantitative measures such as accuracy, precision, recall and F1Score.