Development of a Hyperparameter-Optimized Decision Support System for Cardiovascular Disease Prediction

Uzma Nawaz; Mufti Anees-ur-Rahaman; Hafiz Muhammad Ubaidullah; Chaudhry Muhammad Ali Nawaz; Zubair Saeed

doi:10.47852/bonviewAIA52025361

Authors

Uzma Nawaz Department of Computer and Software Engineering, National University of Science and Technology, Pakistan
Mufti Anees-ur-Rahaman Military College of Signals, National University of Science and Technology, Pakistan
Hafiz Muhammad Ubaidullah Department of Computer Information Systems Engineering, NED University of Engineering and Technology, Pakistan
Chaudhry Muhammad Ali Nawaz Department of Creative Technologies, Software Engineering, Air University, Pakistan
Zubair Saeed Department of Electrical & Computer Engineering, Texas A&M University, USA

DOI:

https://doi.org/10.47852/bonviewAIA52025361

Keywords:

decision support system, cardiovascular disease, physiological data, machine learning, deep learning, XGBoost, parametric values

Abstract

Cardiovascular disease (CVD) remains one of the leading health dilemmas all over the world, and it is among the leading causes of morbidity and mortality. To solve this problem, more than traditional diagnostic methods are needed; smart decision support systems (DSS) are required to help clinicians recognize the condition at an early stage and provide advice on treatment regimens. We develop an improved DSS with multiple machine learning (ML) techniques in cardiovascular risk prediction. Our framework is also focused on clinical applications, as compared to many other prior models that are seen as only theoretically viable. To determine its adequacy, eight ML and deep learning (DL) models were trained and optimized on a clinical set founded on feature selection and hyper-parameter tuning schemes. Among these, the XGBoost classifier exceeded by far the others in terms of accuracy, interpretability, and speed of computation and its operation, and as such would be the best candidate to deploy. Another characteristic of our system is that the Shapley Additive Explanations (SHAP) analysis is applied, which facilitates increasing the confidence of the results by clearly indicating how they may be compiled for clinicians. The benefits of the proposed DSS lie not only in supporting accurate diagnosis but also in translating to real-time reports and recommendations, which are both actionable and supportive of patient management. Additionally, its architecture is scalable and can fit in a variety of healthcare systems and help address the issue of early intervention to reduce the burden of CVD as a whole.

Received: 5 February 2025 | Revised: 18 August 2025 | Accepted: 30 August 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in the CardiovascularDataset repository at https://github.com/zubi00/CardioviscularDataset.

Author Contribution Statement

Uzma Nawaz: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mufti Anees-ur-Rahaman: Methodology, Software, Writing – review & editing. Hafiz Muhammad Ubaidullah: Software validation, Formal analysis, Writing – review & editing. Chaudhry Muhammad Ali Nawaz: Visualization, Investigation, Resources. Zubair Saeed: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.