Main Page
Deanship
The Dean
Dean's Word
Curriculum Vitae
Contact the Dean
Vision and Mission
Organizational Structure
Vice- Deanship
Vice- Dean
KAU Graduate Studies
Research Services & Courses
Research Services Unit
Important Research for Society
Deanship's Services
FAQs
Research
Staff Directory
Files
Favorite Websites
Deanship Access Map
Graduate Studies Awards
Deanship's Staff
Staff Directory
Files
Researches
Contact us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
A ML framework for early detecting the likelihood of cardiovascular disease in a patient using multi-attributes
إطار تعلم الآلة للكشف المبكر عن احتمالية الإصابة بأمراض القلب والأوعية الدموية لدى المريض باستخدام سمات متعددة
Subject
:
Faculty of Computing and Information Technology
Document Language
:
Arabic
Abstract
:
Heart attack is one of the most pressing problems in health care. Heart-related or cardiovascular diseases are the leading cause of many deaths in the world over the past few decades and have emerged as the most life-threatening disease. We need a reliable, accurate, and feasible system for urgently diagnosing such diseases for proper treatment. Nowadays, machine learning is known to play a huge role in the medical industry and the application of machine learning algorithms and techniques on various medical data sets to automate the analysis of large and complex data using various machine learning models for disease diagnosis, classification, or prediction. Results. Several researchers are recently using various machine learning techniques to help the healthcare industry and professionals diagnose heart-related diseases. This research provides an improvement on the factors and triggers that may lead to a heart attack. This research focuses on developing a simplified framework that combines several machine learning techniques such as Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor, Decision tree, and Random Forest to help predict early heart attacks for different age groups using patient data. Both quantitative and qualitative approaches are used, which helped to analyze and evaluate data specifically collected from the Saudi community to conduct this research. The results indicated that the proposed developed framework outperformed the model in the initial stage as it gave SVM greater accuracy in less time to predict with an accuracy of 85.99%. Finally, the framework is evaluated using evaluation criteria, in addition to comparing the work with the previous work.
Supervisor
:
Dr. Farrukh Saleem
Thesis Type
:
Master Thesis
Publishing Year
:
1444 AH
2023 AD
Added Date
:
Monday, May 1, 2023
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
وهج فرحان الشمري
Alshammari, Wahaj Farhan
Researcher
Master
Files
File Name
Type
Description
49176.pdf
pdf
Back To Researches Page