Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
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Myocardial Infarction (MI) is a crucially leading reasons of huge mortality and modality all over the world. The prior reasons for most of the demise are delayed diagnosis and disrupted medical attention. Our endeavor objectifies developing a portable system to detect MI instantly and incorporating a forewarning system using GSM and GPS module. The paper is focusing on the warning system delivering text messages containing classified MI information. Initially, the dataset has been preprocessed using Standard Scalar method and the preprocessed data has been trained and tested using K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes (NB) to distinguish the MI affected ECG from normal ECG signal. The aim of this project is to avail immediate attention to a MI affected patient to ensure medical deliberation rapidly. Proper activation of the system will minimize the deadly effect of MI and hence reduce the mortality rate due to MI.