Optimized Heart Failure Prediction using Support Vector Machine Algorithms

Heart failure remains a leading cause of death globally, highlighting the importance of early intervention and accurate risk assessment. This research investigates the potential of Support Vector Machine (SVM) models, a supervised learning technique, in predicting heart failure. By utilizing a large dataset of clinical characteristics, the study demonstrates the SVM model’s ability to accurately predict heart disease with a classification accuracy of 87.53%. The research highlights the importance of integrating multimodal data sources, including clinical biomarkers, imaging, lifestyle, and demographic information, to enhance prediction accuracy and enable preventive healthcare interventions. Wearable devices, the Internet of Things, and Electronic Health Records (EHRs) contribute to the availability of rich, longitudinal data streams for real-time monitoring and prediction. However, developing reliable and interpretable models for clinical use remains a challenge. Future research should focus on improving model generalization, data accuracy, and interpretability. By addressing these challenges and utilizing real-time data, machine learning and deep learning techniques can significantly advance predictive healthcare, reducing morbidity and mortality, and improving patient outcomes. This research highlights the promise of SVM models in heart failure prediction and underscores the continued need for research to maximize their practical application.