An Approach of CA with M-RIPPER for Heart Disease Prediction

Abstract In the present world, mobile computing devices are popular and are identified in each aspect of life. This combination among computing and the present world is not restricted to the everyday life. The medical field was similarly concerned, where care is given in a wide scope of areas and conditions. The medical domain is continually being immersed with new kinds of innovations, including context-aware system and application. In this research, a context aware healthcare model based on IoT application is proposed. The smart medical devices are used to measure the data from the patients and store it in database. From the database, the patient’s information and medical records are considered as context aware data. For analyzing and classifying the data, the MRIPPER (Modified Repeated Incremental Pruning to Produce ErroR) algorithm is used. This algorithm is a rule-based machine learning algorithm. By using this algorithm, the rules are framed for the analysis of dataset for the prediction of heart disease. The performance analysis of the proposed model is experimented in MATLAB simulation tool. Further, the performance of the proposed model is compared with other existing models like J48, random forest, CART, OneR, and JRip algorithms. The proposed algorithm has achieved 98.89% accuracy, Precision is 96.76%, recall or sensitivity is 99.05%, specificity is 94.35%, and f-score is 97.60%. Overall, the proposed model has obtained 97.38% accuracy in predicting normal class and 97.93% in predicting abnormal class subjects.