Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy

Abstract

Mitigating chronic kidney disease poses a substantial challenge to the healthcare community. This study assesses diverse classification algorithms, encompassing NaiveBayes, multi-layer perceptron, and support vector machine. The analysis involves scrutinizing the chronic kidney disease dataset from the UCI machine learning repository. Techniques like replacing missing values, unsupervised discretization, and normalization are employed for precision enhancement. The empirical results of the classification models are evaluated for accuracy and computational time. The conclusive observation indicates that the support vector machine performs notably better than all other classification methods, with a 76% classifier accuracy which is better than classifiers such as MLP and NB. The lack of application of those feature selection methods to the dataset is a drawback of this study.