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Feature-Limited Prediction on the UCI Heart Disease Dataset

Khadijah Mohammad Alfadli, Alaa Omran Almagrabi*

Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Alaa Omran Almagrabi. Email: email

Computers, Materials & Continua 2023, 74(3), 5871-5883.


Heart diseases are the undisputed leading causes of death globally. Unfortunately, the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart issues. Several potentially indicative factors exist, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, etc. Manually analyzing these health signals’ interactions is challenging and requires years of medical training and experience. Therefore, this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. More specifically, this paper builds a hybrid model as a tool for data mining algorithms like feature selection. The goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy individuals. The contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum information. The developed model achieves 84.24% accuracy, 89.22% Recall, and 83.49% Precision using only a subset of the features.


Cite This Article

APA Style
Alfadli, K.M., Almagrabi, A.O. (2023). Feature-limited prediction on the UCI heart disease dataset. Computers, Materials & Continua, 74(3), 5871-5883.
Vancouver Style
Alfadli KM, Almagrabi AO. Feature-limited prediction on the UCI heart disease dataset. Comput Mater Contin. 2023;74(3):5871-5883
IEEE Style
K.M. Alfadli and A.O. Almagrabi, "Feature-Limited Prediction on the UCI Heart Disease Dataset," Comput. Mater. Contin., vol. 74, no. 3, pp. 5871-5883. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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