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Title CLASSIFICATION OF CARDIOVASCULAR DISEASES (CVDs) USING EXPLAINABLE AI (XAI) BASED ON PHONOCARDIOGRAM (PCG) SIGNALS

Author(s) Muhammad Tahir Javaid

Abstract Cardiovascular diseases (CVDs) are among the leading causes of death worldwide, making early heart examination crucial. Analyzing heart sounds is one of the many key methods for diagnosing cardiac disorders. However, automated classification of heart sounds remains challenging. Phonocardiograms (PCGs) offer a non-invasive method for identifying CVDs by capturing continuous heart sounds, including murmurs. Recent advancements in artificial intelligence (AI) and machine learning (ML) have made it feasible to analyze large volumes of PCG data from cardiac cycles within a reasonable time frame. Researchers have leveraged these technologies in numerous case studies over the past few years to improve detection accuracy and reduce detection time. A comparatively recent shift in this regard is the focus on improving the interpretability and trustworthiness of these AI-driven diagnostic models, a field known as Explainable AI (XAI). XAI is crucial because it not only provides insights into how models make predictions but also fosters trust among clinicians and patients, ensuring that decisions are based on understandable and justifiable reasoning. This transparency is particularly mportant in healthcare, where the consequences of misinterpretations can be significant. This study focuses on feature extraction, classifier selection, and model interpretability for efficient XAI implementation. Three ML classifiers [Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)] are used to predict CVD risk. While all ML models demonstrated good prediction capability RF achieved the best performance with an accuracy of 93.82%, precision of 92.01%, recall of 95.33%, specificity of 92.44%, and an F1 score of 93.64%. Besides critical predictors of long-term CVD risk and its impact on risk prediction are obtained using an explainable techniques for interpreting ML predictions.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2024-12-26

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