Degree
Master of Science in Computer Science
Department
Department of Computer Science
School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Spring 2024
Supervisor
Dr. Imran Khan, Assistant Professor, Department of Computer Science
Keywords
Digital Twin, Cardiovascular Disease (CVD), Metabolic Syndrome, Random Forest Classifier, K-Means Clustering
Abstract
Conventional cardiac care primarily focuses on reactive treatments addressing issues only after they arise, often resulting in increased mortality rates. There is an urgent need to shift towards proactive treatment strategies that enable early intervention, thereby improving patient outcomes and reducing mortality rates. This study proposes a transformative shift towards proactive treatment strategies, enabling early intervention to enhance patient outcomes and reduce mortality rates. The core of this research project is the “Digital Twin for Heart Patients”, an innovative effort to develop a digital twin model of the human heart patient. This project aims to utilize historical patient data to accurately predict and visually represent cardiovascular disease (CVD) risks in real- time, with a particular focus on individuals with “Metabolic Syndrome” — a set of conditions that increase the risk of developing coronary heart disease and other significant health complications. Through the application of predictive analytics, real-time data visualization, and advanced data integration techniques, this initiative seeks to revolutionize cardiac healthcare by improving patient care and advancing proactive healthcare methodologies. This research not only underscores a significant leap in the application of digital twin technology in cardiology but also holds the potential to dramatically transform the paradigms of patient monitoring and treatment planning.
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Siddiqui, Minhaj. "Digital Twin for Heart Patients." Unpublished graduate research project. Institute of Business Administration. 2024. https://ir.iba.edu.pk/research-projects-mscs/53
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