Evaluation of Machine Learning Approaches for Predicting Cardiovascular Attacks

The biggest challenge in the medical field is predicting heart disease sufficiently early to avert mortality. With the development of machine learning, medical data is used to predict heart disease. The clinical outcome can be foreseen by evaluating the precision and effectiveness of supervised machine learning algorithms including Support vector machine learning algorithm, Decision Tree Classifier and, Random Forest algorithm. The performance of the machine learning algorithm could be analyzed with statistical metrics as F1 score, recall, precision and accuracy. The objective is to find an optimal algorithm to diagnose problems in health sector.