Work place: Department of Computer science,University College of Khurma, Taif University, KSA
E-mail: kharoubi.naoufel@gmail.com
Website: https://orcid.org/0000-0002-9390-1259
Research Interests:
Biography
Kharroubi Naoufel Engineering Sciences and Technologies in 2007 and the Ph.D. degree in Computer
Science in 2010 from Saint Petersburg Electrotechnical University “LETI,” Russia. His major field of
study was ontological approaches for compatibility in complex engineering systems.
He has more than fifteen years of experience in higher education, research, and technological development.
From 2007 to 2008, he was a Lecturer with the Department of Computer Technologies and Informatics
(FKTI), Saint Petersburg Electrotechnical University “LETI,” Russia. From 2009 to 2014, he was a
Contractual Lecturer at ISET Kairouan, Tunisia. Since 2014, he has been a Contractual Assistant Professor
with the Department of Computer Science, Alkhurma University College, Taif University, Saudi Arabia,
where he teaches Python programming, databases, web development, artificial intelligence, expert systems,
software engineering, and C/C++ programming. He is the author and coauthor of several journal and conference publications in the
areas of ontological engineering, reconfigurable systems, and complex systems modeling. His current research interests include
complex systems engineering, ontologies and automated reasoning, and full-stack web development using Python/Django, REST
APIs, and cloud technologies.
Dr. Kharroubi is an academic researcher and educator. He has participated in several international conferences and scientific
publications. His work has been published in indexed international journals and conferences.
DOI: https://doi.org/10.5815/ijem.2026.02.09, Pub. Date: 8 Apr. 2026
The study applies machine learning algorithms to the diagnosis of heart disease. Data were collected from multiple sources in hospital and clinic records, along with time-based comparison other studies. The second part of the study, Clinical Decision Support, simulated the daily work of a physician and helped them make patient-centered medical decisions. The results revealed significant potential for machine learning to improve heart disease detection efficiency and accuracy, which could benefit future effective disease management and reduce patient burden. The study findings will enable future healthcare providers to harness new technology to achieve better prevention and superior care outcomes for heart disease screening. The study recommendations include optimal diagnostic skills and intervention-oriented preventive measures.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals