International Journal on Advanced Science, Engineering and Information Technology, Vol. 12 (2022) No. 5, pages: 1887-1894, DOI:10.18517/ijaseit.12.5.16015

Heart Disease Prediction Using Genetic Algorithm with Machine Learning Classifiers

Basant Abdel Menem Metwally, Nagham Elsayed Mekky, Ibrahim Mahmoud Elhenawy


Based on the world health organization (WHO), heart disease is the major reason for the loss of life everywhere on earth. Heart attacks are the leading reason of loss of life among heart diseases. This disease is called a silent disease. The person does not feel any pain until the last level of sickness and may arrive at death if not saved at the right time. The datasets for this disease are becoming available, so it is a particularly good branch of study. Predicting a heart attack for a medical practitioner is difficult since it requires increased expertise. However, over the last few decades, resolving complicated, extremely non-linear classification and prediction problems have been using machine learning algorithms (ML). Hence, it is feasible to establish a prediction model that would see the existence or nonexistence of heart disease based on many heart-related symptoms (features). The essential contribution of this research is to introduce various prediction models for heart disease using a genetic algorithm (GA) to find optimal features combined with classical machine learning techniques. The optimized prediction model uses a genetic algorithm that performs better than classical models. The execution of the algorithms is tested using Cleveland and Framingham datasets. The prediction models' performance is standardized using three evaluation criteria: accuracy, precision, and recall. The proposed system showed superior performance compared with other related systems. It reached an accuracy of 100% for the Cleveland dataset and 91.8% for the Framingham dataset.


Heart attacks; genetic algorithm; machine learning; K-Nearest Neighbor classifier; random forest.

Viewed: 644 times (since abstract online)

cite this paper     download