Health Care Decision Support System for Swine Flu Prediction Using Naïve Bayes Classifier

The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. However there is ongoing research in medical diagnosis which can predict the diseases of the heart, lungs and various tumors based on the past data collected from the patients. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data for Swine Flu. This research has developed prototype Intelligent Swine flu Prediction software (ISWPS). We used Naïve Bayes classifier for classifying the patients of swine flu into three categories (least possible, probable or most probable). We have used 17 symptoms of Swine flu and collected 110 symptoms sets from various hospitals and medical practitioners. Using ISWPS, we have achieved an accuracy of nearly 63.33%. It is implemented on the JAVA platform.