Heart Attack Prediction Using Random Forest Classifier Approach

Abstract

Heart attack accounts for about 20-25 million deaths worldwide. However, 80% of these deaths are premature deaths and can be reversed by introducing new-age tools in biomedical diagnosis. Machine learning, one of the new age tools, offers a better, cost-effective approach to minimizing these deaths. In the past, machine learning could analyze vast amounts of data, including health records and scans, and identify patterns. This allows researchers to develop a model based on their results. Utilizing ML In this chapter, the authors proposed a hybrid model named random forest boosting classifier (RFBC), a combination of random forest and gradient boosting classifiers. The proposed model achieved an accuracy of 91.8%. The dataset used was the standard Cleveland heart disease dataset. The results indicated that despite advancements in the medical field, combining ML with medical diagnosis techniques remains an easy-to-implement solution.