Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective

TY - JOUR

T1 - Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective

AU - Alwakid, Ghadah

AU - Ul Haq, Farman

AU - Tariq, Noshina

AU - Humayun, Mamoona

AU - Shaheen, Momina

AU - Alsadun, Marwa

PY - 2025/2/20

Y1 - 2025/2/20

N2 - Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification. It uses methodologies such as Chi-square, Info Gain, Forward Selection, and Backward Elimination as an essence of cardiovascular health indicators into a refined eight-feature subset. This study emphasizes ethical considerations, including transparency, interpretability, and bias mitigation. This is achieved by employing unbiased datasets, fair feature selection techniques, and rigorous validation metrics to ensure fairness and trustworthiness in the AI-based diagnostic process. In addition, the integration of various Machine Learning (ML) models, encompassing Random Forest (RF), XGBoost, Decision Trees (DT), and Logistic Regression (LR), facilitates a comprehensive exploration of predictive performance. Among this diverse range of models, XGBoost stands out as the top performer, achieving exceptional scores with a 99% accuracy rate, 100% recall, 99% F1-measure, and 99% precision. Furthermore, we venture into dimensionality reduction, applying Principal Component Analysis (PCA) to the eight-feature subset, effectively refining it to a compact six-attribute feature subset. Once again, XGBoost shines as the model of choice, yielding outstanding results. It achieves accuracy, recall, F1-measure, and precision scores of 98%, 100%, 98%, and 97%, respectively, when applied to the feature subset derived from the combination of Chi-square and Forward Selection methods.

AB - Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification. It uses methodologies such as Chi-square, Info Gain, Forward Selection, and Backward Elimination as an essence of cardiovascular health indicators into a refined eight-feature subset. This study emphasizes ethical considerations, including transparency, interpretability, and bias mitigation. This is achieved by employing unbiased datasets, fair feature selection techniques, and rigorous validation metrics to ensure fairness and trustworthiness in the AI-based diagnostic process. In addition, the integration of various Machine Learning (ML) models, encompassing Random Forest (RF), XGBoost, Decision Trees (DT), and Logistic Regression (LR), facilitates a comprehensive exploration of predictive performance. Among this diverse range of models, XGBoost stands out as the top performer, achieving exceptional scores with a 99% accuracy rate, 100% recall, 99% F1-measure, and 99% precision. Furthermore, we venture into dimensionality reduction, applying Principal Component Analysis (PCA) to the eight-feature subset, effectively refining it to a compact six-attribute feature subset. Once again, XGBoost shines as the model of choice, yielding outstanding results. It achieves accuracy, recall, F1-measure, and precision scores of 98%, 100%, 98%, and 97%, respectively, when applied to the feature subset derived from the combination of Chi-square and Forward Selection methods.

KW - Machine learning

KW - Cardiovascular diseases

KW - Chi-square

KW - Principal component analysis

KW - K-nearest neighbours

KW - Logistic regression

KW - Decision trees

KW - Random forest

KW - Artificial intelligence

KW - Feature selection

KW - XGBoost

U2 - 10.1186/s12872-025-04550-w

DO - 10.1186/s12872-025-04550-w

M3 - Article

SN - 1471-2261

VL - 25

JO - BMC Cardiovascular Disorders

JF - BMC Cardiovascular Disorders

IS - 1

ER -