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Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
Using Machine Learning Techniques to Predict Heart Disease
استخدام تقنيات تعلم الآلة للتنبؤ بأمراض القلب
Subject
:
Faculty of Computing and Information Technology
Document Language
:
Arabic
Abstract
:
Heart diseases are the undisputed leading causes of death in the world. Unfortunately, the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart issues. Many influential factors are challenging to analyze, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, and many others. Our contribution in this field is to provide patients with accurate and timely results to help prevent further complications and heart attacks, which is lacking in current research. This work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. Therefore, we will focus on deep learning methods and data mining algorithms like feature selection to determine the most critical factors that can indicate heart illnesses. The developed model achieves 84.24% accuracy, 89.22% Recall, and 83.49% Precision using only a subset of the features. Keywords: machine learning, feature selection, heart disease
Supervisor
:
Dr. Alaa Almaghrabi
Thesis Type
:
Master Thesis
Publishing Year
:
1445 AH
2023 AD
Added Date
:
Sunday, October 15, 2023
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
خديجة محمد الفضلي
Alfadli, Khadijah Mohammed
Researcher
Master
Files
File Name
Type
Description
49378.pdf
pdf
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