Multi-Parameter Based Performance Evaluation of Classification Algorithms

Diabetes disease is amongst the most common disease in India. It affects patient's health and also leads to other chronic diseases. Prediction of diabetes plays a significant role in saving of life and cost. Predicting diabetes in human body is a challenging task because it depends on several factors. Few studies have reported the performance of classification algorithms in terms of accuracy. Results in these studies are difficult and complex to understand by medical practitioner and also lack in terms of visual aids as they are presented in pure text format. This reported survey uses ROC and PRC graphical measures to improve understanding of results. A detailed parameter wise discussion of comparison is also presented which lacks in other reported surveys. Execution time, Accuracy, TP Rate, FP Rate, Precision, Recall, F Measure parameters are used for comparative analysis and Confusion Matrix is prepared for quick review of each algorithm. Ten fold cross validation method is used for estimation of prediction model. Different sets of classification algorithms are analyzed on diabetes dataset acquired from UCI repository.