The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease.

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[49] Vijaya Saraswathi R., Gajavelly K., Kousar Nikath A., Vasavi R., and Reddy Anumasula R., “Heart Disease Prediction Using Decision Tree and SVM,” in Proceedings of the 2nd International Conference on Advances in Computer Engineering and Communication Systems, Hyderabad, pp. 69-78, 2022. https://link.springer.com/chapter/10.1007/978- 981-16-7389-4_7#citeas [50] Yang J., Li Y., Liu Q., Li L., Feng A., Wang T., Zheng S., Xu A., and Lyu J., “Brief Introduction of Medical Database and Data Mining Technology in the Big Data Era,” Journal of Evidence-based Medicine, vol. 13, no. 1, pp. 57-69, 2020. https://doi.org/10.1111/jebm.12373 Alaa Sheta obtained his Ph.D. in Information Technology from George Mason University, Virginia, USA, in 1997. Before that, he earned his B.E. and M.Sc. degrees in Electronics and Communication Engineering from Cairo University, Egypt in 1988 and 1994, respectively. He holds a tenured professorship at the Computer Science Department of Southern Connecticut State University in New Haven, Connecticut, USA. Dr. Sheta’s research interests span various areas, including machine and deep learning, meta-heuristics, data science, image processing, and robotics. He has significantly contributed to these fields, publishing more than 170 papers in international journals and conferences. In addition to his scholarly achievements, he has actively participated in various capacities, such as serving as a chair, guest editor, and program committee member for numerous international events. Walaa El-Ashmawi is an Associate Professor at the Faculty of Computer Science, Misr International University, on leave from Suez Canal University, Egypt. She received her B.Sc. in 2001 and M.Sc. in 2008 from Egypt. She got her Ph.D. She earned her degree in 2013 from the Computer Science Department, College of Information Science and Engineering, Hunan University, China. Her research interests Span Various Domains in Computer Science including Artificial and Computational Intelligence, Meta-heuristic algorithms Optimization problems, and Machine Learning techniques. She authorizes over 40 published papers in various international journals and conferences. She led different administrative and academic positions, both full-time and part-time. AbdelKarim Baareh holds a Doctor of Informatics/Artificial Intelligence degree from Damascus University, Syria, which he earned in 2009. He also completed his B.Sc. and M.C.A (Computer Application) degrees from Mysore and Bangalore University, India, in 1992 and 1999, respectively. Professor Baara is on sabbatical leave and affiliated with the Data Science and Artificial Intelligence Department at the Information Technology College, Isra University in Jordan. Additionally, he holds a permanent academic position within the Computer Science Department at Al- Balqa Applied University, Jordan. Dr. Baara’s extensive research portfolio spans various domains, including meta-heuristics, global optimization, machine learning, data mining, bioinformatics, graph theory, and parallel programming. Over the years, he has contributed significantly to his field, with a publication record that boasts over 35 papers in international journals and conferences.