Machine learning-based predictive models for cardiovascular risk assessment in data analysis, model development, and clinical implications

Singarathnam, Dharshika, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090, Pokhrel, Sangita ORCID: https://orcid.org/0009-0008-2092-7029 and Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251 (2023) Machine learning-based predictive models for cardiovascular risk assessment in data analysis, model development, and clinical implications. International Journal of Recent Advances in Multidisciplinary Research, 10 (10). pp. 9084-9089.

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Abstract

Cardiovascular diseases (CVDs) remain a leading global cause of morbidity and mortality. Timely identification of individuals at risk is paramount for effective interventions and prevention. This study endeavors to develop machine learning approaches for predicting the initial cardiovascular risk level analyzing the dataset encompassing patient demographics, medical history, lifestyle factors, and clinical indicators. Patient characteristics, including age, gender, diabetes or hypertension presence, smoking status, and physical activity level, along with medical indicators such as blood pressure, cholesterol, and glucose levels, are considered. Diverse machine learning algorithms—logistic regression, decision tree classifier, random forests, linear SVC, naive bayes, and neural network—are employed to train and optimize predictive models. Evaluation metrics (accuracy, precision, recall, F1 score, and AUC-ROC) assess model performance. Accurate risk prediction models hold significance in aiding healthcare decisions, optimizing resource allocation, and enhancing patient outcomes. Identifying high-risk individuals early enables preventive strategies and personalized interventions, reducing the CVD burden. Study objectives encompass dataset preprocessing, exploratory analysis, feature selection and engineering, model training and optimization, and performance evaluation. Findings contribute to cardiovascular risk prediction, presenting a robust model for accurate risk assessment and improved patient outcomes.