Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms‏
Paper ID : 1057-ICEEM2021 (R3)
Mohamed G. El-Shafiey1, Ahmed Hagag *2, El-Sayed A. El-Dahshan3, Manal A. Ismail4
1Faculty of Computers and Information Technology, Egyptian E-Learning University, Dokki, Giza, 12611, Egypt
2Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt
3Department of Physics, Faculty of Science, Ain Shams University, Abbasia, 11566, Egypt
4Faculty of Engineering, Helwan University, Helwan, 11731, Egypt
Today, heart-disease is one of the most significant causes of mortality in the world. Thus, the prediction of heart-disease is a critical challenge in the area of healthcare systems. In this study, we aim to select the optimal features that can increase the accuracy of heart-disease prediction. A feature-selection algorithm, which is based on genetic algorithm (GA) and random forest (RF), is proposed to increase the accuracy of RF-based classification and determine the optimal heart-disease-prediction features. The performance of the proposed approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the ROC curve by using a public dataset from the University of California, namely, Cleveland. The experimental results confirm that the proposed approach attained the high heart-disease-prediction accuracies of 95.6% on the Cleveland dataset. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.
Cleveland dataset, Feature selection (FS), Genetic algorithm (GA), Heart-disease prediction, Random forest (RF).
Status : Paper Accepted