Comparative Study of Chronic Kidney Disease Prediction using Machine Learning Techniques - Jadhav, Sayali; Chandran, Priya; Vijaykumar, Suhasini (International Journal of Computer Sciences and Engineering, 2019)
The healthcare industry is producing massive amount of data which need to be mine to discover hidden information for effective prediction, exploration, diagnosis and decision making. Chronic kidney disease (CKD), also known as chronic renal disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. Early detection and treatment can often keep chronic kidney disease from getting worse. Machine learning techniques are commonly used to predict this situation. This research work mainly focused on finding the best classification algorithm based on different evaluation criteria like performance accuracy and root mean square error. We have performed a comparative study of the performance of machine learning algorithms J48, Support Vector Machine and Multilayer perceptron. The results show that MLP is giving minimum root mean square error value compared to J48 and SVM.