Predictive Modelling of cardiovascular Disease Survival Using Mutual Information and Machine Learning Across Varying Sample Sizes
Year: 2025
Type: article
Abstract: In clinical data analytics, predicting survival outcomes for cardiovascular disease (CVD) is a challenging task with practical implications. Using three different datasets, this study investigates how sample size affects machine learning performance and generalizability. The methodology combines statistical sample-size analysis with mutual information gain, a filter-based, scalable, and domain-agnostic feature selection strategy, to identify clinically essential features. Mutual information gain measures the dependency between each predictor and the target variable, ensuring computational efficiency and applicability to large-scale data. Machine learning classifiers, support vector machines (SVMs) and logistic regression (LR), are employed to assess predictive performance across varying population sizes. Experimental results demonstrate that increasing the sample size improves model accuracy by up to 10%, recall by 5–8%, and maintains consistent specificity. Furthermore, to enhance clinical reliability, the models are evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), where SVM achieved an AUC of 0.965 and LR achieved 0.937, validating strong discriminatory power; Also, SHAP-based feature attribution is used to improve interpretability, identifying that larger sample sizes provide more stable and clinically meaningful explanations.
DOI: 10.14419/xn4vbz19
Institution: Birla Institute of Technology, Mesra
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4413220221