Comparative Study of Machine Learning Algorithm to Predicting Diabetics Kidney Disease

Shweta Yadu; Mahadev Bag

doi:10.59367/t4crg547

Authors

Shweta Yadu Computer science and engineering Raipur Institue of Technology (CSVTU) Raipur, Chhattisgarh
Mahadev Bag Computer science and engineering Raipur Institue of Technology (CSVTU) Raipur, Chhattisgarh

DOI:

https://doi.org/10.59367/t4crg547

Keywords:

Machine Learning, Classifier, Random Tree, adaBoost, IBK, Diabetic Kidney Disease Prediction

Abstract

Diabetic Kidney Disease (DKD) poses a significant health concern globally, impacting 20-40% of individuals affected by diabetes. This research presents a comprehensive comparative study aimed at predicting DKD onset using machine learning algorithms. The main goals of the project are to use medical data to identify people who are at risk and to construct a software system that can predict the chance of a disease developing in the future. Several machine learning classification methods, such as IBK, Random Tree, Random Forest, Naive Bayes, and adaBoostM1, are used in the study technique. By utilizing the WEKA machine learning software, these algorithms are put through a rigorous comparison process in order to ascertain their prediction effectiveness. The study finds that IBK and Random Tree classification are the best-performing techniques using 10-fold cross-validation. They show an accuracy of 93.6585% and a higher K value (0.8731). This comparative research demonstrates how machine learning approaches may be used to predict DKD onset accurately, which can lead to proactive intervention options.

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