Machine Learning Based Improved Heart Disease Detection with Confidence

Abstract

One of the hardest jobs in medicine is to predict when someone will have a heart attack. Given how challenging it is to anticipate heart attack, there is an urgent need to automate the prediction process using diagnostic data, and at the very least generate an early warning. This research makes a contribution by making it easier to diagnose cardiac problems using machine learning methods applied on the well-known Cleveland heart disease dataset. Several performance indicators are utilized to evaluate each model's strength. It turns out that support vector machine and random forest produced some incredibly promising outcomes. An improved prediction of heart disease for an embedded platform is, thus, proposed, based on the computational complexity of each model and experimental results, where the advantages of several classifiers are accumulated. The approach suggests that, and only if, more than one of these classifiers detect heart disease, the detection of heart illness is possible with increased confidence. In the end, experimental findings are drawn to a conclusion, with potential future options for advancing this effort.