ABSTRACT
An early diagnosis and expedited medical intervention are critical factors in reducing mortality rates associated with Myocardial Infarction (MI). Machine Learning (ML) programs perform better with experienced operators. In this proposal, experiences and information of expertise is used, as is knowledge of the victims of the disease, thereby to answer queries along with study of numerous algorithmic programs that improved mechanically through expertise, and this was possible through ML. The information of the algorithm that may offer the most effective result to classify knowledge regarding MI, Support Vector Machine (SVM) that performs well compared with an alternative ML algorithm such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and so forth were worked with. The model’s accuracy depends on the datasets available, and the features used to predict the model. If SVM doesn’t give better accuracy, then other models such as an ensemble algorithm, neural network can be applied for a better diagnosis from the model.
