Date of Award

5-2025

Document Type

Thesis

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Dr. Conrad Shayo

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating the development of accurate and interpretable machine learning (ML) models for early diagnosis and risk assessment (World Health Organization, 2021). While ML algorithms such as logistic regression, decision trees, support vector machines (SVM) (Cortes & Vapnik, 1995), and deep learning models (LeCun et al., 2015) have demonstrated high predictive accuracy, their adoption in clinical practice is hindered by their black-box nature (Rudin, 2019). Explainable AI (XAI) techniques, including SHapley Additive Explanations (SHAP) (Lundberg & Lee, 2017), Local Interpretable Model-agnostic Explanations (LIME) (Ribeiro et al., 2016), and feature importance analysis aim to bridge this gap by improving model transparency and interpretability (Doshi-Velez & Kim, 2017). This study explores the effectiveness of various ML algorithms for heart disease prediction and examines how XAI techniques enhance their interpretability. Furthermore, it investigates the role of model transparency in influencing clinician trust and adoption of AI-based diagnostics. Research indicates that interpretable AI models foster greater trust among healthcare professionals, as they align with established medical knowledge and facilitate informed decision-making. Despite these benefits, challenges such as computational complexity, trade-offs between accuracy and interpretability, and ethical concerns regarding patient data privacy persist. The findings highlight the necessity of integrating XAI techniques into ML models to ensure high accuracy and transparency in heart disease prediction. By addressing existing challenges, AI-driven diagnostics can be effectively incorporated into clinical workflows, ultimately improving patient outcomes and supporting evidence-based medical decision-making.

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