Detection of myocardial infarction on recent dataset using machine learning

Nusrat Parveen, Satish R Devane, Shamim Akthar


In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and efficiency of the existing model. Additional parameters are used to predict MI with more accuracy. The proposed model is used to predict an early diagnosis of MI with the help of expertise experiences and data gathered from hospitals.


Decision tree; Ensemble algorithm; Multi-layer perceptron; Myocardial infarction; Naïve Bayes; Neural network; Support vector machine

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