A Comparative Study of Performance Metrics of Data Mining Algorithms on Medical Data

Abstract

Computers have brought about significant technological improvements leading to the creation of enormous volumes of data, particularly in health care systems. The availability of vast amounts of data contributed to a greater need for data mining techniques to produce useful knowledge. Accurate analyzes of medical data are gaining early detection of illness and patient care with the increase of data in biomedical and health care communities. The data mining is one of the major approaches for developing sophisticated algorithms for classification of data. Some have castigated Data mining for not meeting all of the humanistic statistics specifications [5]. Classification of diseases is that one of the main applications of data mining and many important attempts have been made in recent years to improve the accuracy of the diagnosis of diseases through data mining. We used four prominent data mining algorithms such as Naive Bayes Classifier, K-Nearest Neighbors (KNN) Classifier, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms to develop predictive models using that the ILPD (Indian Liver Patient Data Set) from the UCI Machine learning repository. For performance comparison purposes, we used the 10-fold cross validation method to calculate the estimation of six predictive models. We find that the support vector machine delivers the best results in a 74.82 percent accuracy classification and 56.55 percent accuracy of the Naive Bayes performed the worst. The performance metrics of classifiers were analyzed on medical dataset further sections below.