Comparative study of various machine learning classifiers on medical data

Abstract

Data classification is an important task to label the class of data. Attributes or feature is a portion of information which is applicable to the task of computation. Our task is to predict and prevent cardiac arrest which is one of the biggest challenges of cardiology using a machine learning classifier. Since a particular classifier may or may not work well for such datasets so it is important to do a comparative study of classifiers in order to achieve maximum performance in such critical predictions of cardiac arrest. The UCI dataset is chosen for the purpose of comparison, and a comparative study of various classifiers is provided on the same dataset. Results are given as accuracy of different classifiers. The various classifier methods include the KNN classifier, Nave Bayes classifier, Support Vector Machine, Neural Network, Gaussian Mixture Model and Decision Tree classifier.