Machine Learning Approaches for Early Prediction of Cardiovascular Diseases

Kyunghyun Cho

Machine Learning Approaches for Early Prediction of Cardiovascular Diseases

Author(s)	Kyunghyun Cho
Country	United States
Abstract	Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually and imposing a substantial burden on healthcare systems. Early prediction and timely intervention are critical for reducing disease progression, preventing complications, and improving patient survival. Recent advances in Machine Learning (ML) have enabled the development of intelligent predictive models capable of analysing complex clinical, demographic, imaging, laboratory, genomic, and wearable sensor data to identify individuals at high risk of cardiovascular diseases before the onset of severe symptoms. Machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Naïve Bayes, Gradient Boosting, XGBoost, Artificial Neural Networks (ANN), and Deep Learning models have demonstrated promising performance in cardiovascular risk prediction, diagnosis, prognosis, and personalised treatment planning. This study investigates machine learning approaches for the early prediction of cardiovascular diseases using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international clinical guidelines, healthcare reports, and multidisciplinary case studies. The study examines feature selection methods, data preprocessing techniques, predictive modelling, explainable artificial intelligence (XAI), electronic health records (EHRs), wearable health monitoring devices, Internet of Things (IoT), federated learning, and cloud-based healthcare analytics. Furthermore, it evaluates the contribution of machine learning to clinical decision support, personalised medicine, telecardiology, preventive healthcare, and intelligent healthcare management while identifying implementation challenges and future research opportunities. The findings indicate that machine learning significantly improves the accuracy of cardiovascular risk prediction, early disease detection, clinical decision support, patient monitoring, and healthcare resource optimisation. Explainable AI techniques such as SHAP and LIME enhance clinician trust by providing transparent interpretations of model predictions. Integration of IoT-enabled wearable devices, federated learning, blockchain, Digital Twins, and multimodal healthcare analytics further strengthens predictive performance while protecting patient privacy. However, challenges including data quality, class imbalance, model interpretability, privacy concerns, algorithmic bias, interoperability, and regulatory compliance continue to influence successful implementation. The study concludes that machine learning provides a comprehensive multidisciplinary framework for the early prediction of cardiovascular diseases. Collaboration among clinicians, data scientists, biomedical engineers, policymakers, and healthcare organisations is essential to establish trustworthy, accurate, and patient-centred AI-assisted cardiovascular care.
Keywords	: Machine Learning, Cardiovascular Diseases, Early Prediction, Artificial Intelligence, Clinical Decision Support, Explainable AI, Healthcare Analytics, Predictive Modelling, Electronic Health Records.
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-03-19

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Approaches for Early Prediction of Cardiovascular Diseases

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