Comparison of Naive Bayes and Least-Square Support Vector Machine classifier for prediction of Swine Flu

摘要

Disease prediction has long been regarded as a critical topic. Artificial intelligence and machine learning techniques have already been developed to solve this type of medical care problem. Our research focuses on this aspect of medical diagnosis by learning pattern through the collected data for swine flu. This research has developed naive bayes and least-square support vector machine (LS-SVM) classifier for predicting the presence or absence of swine flu. We have generated 96 symptoms sets after consulting with medical practitioners from various hospitals of Punjab, INDIA. Using LS-SVM, we have achieved better prediction accuracy (100%) as compared to naive bayes model . This assessment presents the importance and advantages posed by LS-SVM model for prediction of biological variables.