A Comparison of Different Machine Learning Models for Cardiovascular Disease Detection

Abstract: Cardiovascular disease (CVD) is the leading cause of death worldwide and the majority of the deaths occur in low to middle income countries. This makes the prevention of CVDs an accute problem to study and much research has been done already. New techniques using computer aided diagnostics with the help of machine learning (ML) might have great potential in the future of diagnostics of CVDs but the area of research is relatively new. In this report we compare three distinct models of machine learning, Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Decision Trees (DT). We compare their current classification accuracy and potential viability in CVD classification. Ensemble learning and other model specific optimizations were out of scope for this report and a more general and basic implementation was used. Our results does not indicate a clear winner and all models have different pros and cons. The average accuracy did not differ much between the different models. We found that the SVM gave the highest average classification accuracy while the ANN had similar peek classification accuracy however a slightly lower average classification accuracy. The DT however gave the most interpretable results since the trained model can be easily visualized which made us conclude that DTs are today perhaps the most viable option to be used, as a complement to physicians in their current methods of diagnostics. These report results are although limited by the dataset which was too small for any wide stretching and general conclusions.