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 TypeDr GradeEmail
خديجة محمد الفضليAlfadli, Khadijah MohammedResearcherMaster 

Files

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 49378.pdf pdf 

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