Optimizing cardiovascular disease diagnosis: A meta-heuristic and fuzzy logic-based approach

Cardiovascular disease (CVD) is one of the most common and major global health challenges, which requires improved methods for early and precise detection and intervention. So, to recognize these heart problems and avoid sudden cardiac arrest, it is essential to detect abnormal heart conditions early. Machine learning (ML) based medical treatments are being implemented that are very helpful in quickly and effectively diagnosing CVD problems. One method that can offer practical answers to these kinds of problems is a meta-heuristic approach. Owing to its effectiveness, metaheuristic approaches are presently used with medical data to diagnose conditions more practically and successfully than the traditional ML methods. In this study, we used three different meta-heuristic algorithms which are Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO) for diagnosis of the CVD diseases using two different datasets - CVD and Framingham. Finally, various ML classifiers were applied on the best selected features for both the datasets, obtained from the meta-heuristic algorithms for finding efficiency and comparing the results. The results demonstrate that Framingham dataset gives best accuracy of 98.47% by using CSA algorithm for feature selection and Random Forest as classifier whereas for the CVD dataset gives best accuracy of 94.12% by using PSO algorithm and Random Forest as classifier. Then, the best performing model is passed through some fuzzy logic rules to improve the model accuracy and gives better prediction for CVD prediction.