Survival Prediction of Myocardial Infarction Disease using Cloud Assistance - Patil, C.N; Sumana, M (International Journal of Computer Sciences and Engineering, 2025)
E-healthcare system have been increasingly facilitating health condition monitoring, early intervention and evidence based medical treatment by accepting the Personal Health Information (PHI) of the patients. Myocardial Infarction is one of the most leading cause variations in the health condition and that can be lead to the death of the human being. The early prediction of such disease can reduce or prevent development of it and helps to take the necessary treatment. The proposed system is one efficient tool to predicting such diseases. This system can learn from the past data of those patients to be capable of predicting the survival of death of the patient with myocardial infarction. The health information data of the patients who are suffering from such disease is collected and stored. It consists survival period and some clinical data of patients who suffered from myocardial infarction can be used to train an intelligent system to predict the survival or death of current myocardial infarction patients. The Gaussian Naïve Bayes algorithms are used to train the collected data of patients and generalize the survival or death of current patients suffering from myocardial infarction. Experimentally, the instances are stored in the cloud and used as the trained instance. The test data will be provided by the physician to predict the survival or death of the current patient suffering from myocardial infarction.