Intelligent Decision Support System Based on Heart Failure

Authors

DOI:

https://doi.org/10.3991/ijoe.v21i11.56089

Keywords:

Machine Learning, healthcare, Artificial Intelligence, Big Data, Prediction, Logistic Regression

Abstract


In the ever-changing realm of healthcare, the integration of advanced technologies and the wealth of medical data presents exciting opportunities to enhance diagnostic processes. This study presents the Intelligent Diagnostic Decision Support System (IDDSS), employing data analysis and AI to aid doctors in making informed diagnostic decisions. The IDDSS harnesses a wealth of patient data, encompassing medical records, test findings, and demographic details. By employing advanced data analysis techniques, it reveals valuable insights, identifies patterns, and establishes correlations within the data. Furthermore, to elevate diagnostic precision, the IDDSS integrates state-of-the-art AI algorithms and machine learning models. These models, trained on extensive datasets, excel in recognizing intricate patterns, categorizing illnesses, and predicting outcomes. Continuously adapting to incorporate the latest medical advancements, the IDDSS remains at the forefront of enhancing healthcare efficacy and patient care. This system has significant potential to advance healthcare diagnostics, ultimately serving as a valuable decision support tool enabling physicians to provide exceptional care and improve patient outcomes.

Author Biographies

Abderrahmane Ez-Zahout, Mohammed V University in Rabat, Rabat, Morocco

Currently holding the position of assistant professor of computer science at the Department of Computer Science within the Faculty of Sciences at Mohammed V University, he specializes in computer science, digital systems, big data, and computer vision. Lately, his focus has shifted towards intelligent systems. He has contributed as a reviewer for numerous scholarly journals and actively participates in NGOs, student associations, and the management of a non-profit foundation. For inquiries, you can reach him via email at: abderrahmane.ezzahout@um5.ac.ma

Soumia Ziti, Mohammed V University in Rabat, Rabat, Morocco

Dr Ziti is a full professor and researcher at the Faculty of Sciences of Mohammed V University in Rabat since 2007. She obtained her PhD in computer science specializing in graph theory from the University of Orleans in France, along with a diploma in advanced studies in fundamental computer science. She also holds a Baccalaureate in Mathematical Sciences and completed her undergraduate studies in mathematics and physics, specializing in mathematics, at Hassan II University in Morocco. Furthermore, she earned a master's degree in science and technology in computer science from the same institution. Her research interests encompass a wide range of topics including graph theory, information systems, artificial intelligence, data science, software development, database modelling, big data, cryptography, and numerical methods and simulations. Pr. Ziti has contributed extensively to these fields with over than eighty publications in esteemed international journals and conferences. Additionally, she plays a pivotal role in coordinating, participating or assessing in various educational and socio-economic or research projects. you can reach her via email at: s.ziti@um5r.ac.ma

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Published

2025-09-17

How to Cite

Amara, G., Ez-Zahout, A., & Ziti, S. (2025). Intelligent Decision Support System Based on Heart Failure. International Journal of Online and Biomedical Engineering (iJOE), 21(11), pp. 97–115. https://doi.org/10.3991/ijoe.v21i11.56089

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Section

Papers