• N. Komal Kumar
• R. Subhashree
• S. Swetha

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose anensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.

CKD, C4.5, AdaBoost, ensemble, machine learning

"An Ensemble Multi-Model Technique for Predicting Chronic Kidney Disease ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.395-399, March-2019, Available :http://www.jetir.org/papers/JETIRAU06055.pdf

"An Ensemble Multi-Model Technique for Predicting Chronic Kidney Disease ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp395-399, March-2019, Available at : http://www.jetir.org/papers/JETIRAU06055.pdf