A Real-Time Prediction of Adult Congenital Heart Disease Based On Chronic Kidney Disease Using Meta-Heuristics Algorithm

Abstract

Kidney damage and diminished function that lasts longer than three months is known as Chronic Kidney Disease (CKD). Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this research paper, a real-time prediction scheme for adult congenital heart disease (ACHD) based on chronic kidney disease (CKD) using meta-heuristics algorithm (RTPC-ACHD) is proposed. An optimal data mining technique to accurately predict the target class for each case in the data is needed. First, the chaotic fuzzy multi-model neural network is used for the early prediction of heart diseases using CKD risk factors. The present work emphasize is based on data mining and the classification techniques in health informatics to detect Chronic Kidney Disease (CKD). Self-adaptive Bat optimization algorithm is a novel meta-heuristics algorithm which selects the most optimum features which contribute more to the result which reduces the computation time and increases the accuracy. The experimental results shows that the proposed RTPC-ACHD scheme gives a better result than the other classification algorithms and produces 99.17% accuracy.