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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 12 Issue 10, October 2023 | Rating: 4.4 / 10
Machine Learning Innovations in Early Cardiovascular Disease Detection
Abstract: Identifying and predicting the risk of Cardiovascular Diseases (CVD) in healthy individuals is crucial for effective disease management. Leveraging extensive health data available in hospital databases offers significant potential for early detection and diagnosis of CVD, which can greatly improve disease outcomes. Integrating machine learning techniques shows considerable promise in advancing clinical practices for CVD management. These methods enable the development of evidence-based clinical guidelines and management algorithms, potentially reducing the need for costly and extensive clinical and laboratory investigations, thereby easing financial burdens on patients and healthcare systems. To enhance early prediction and intervention for CVD, this study proposes the development of novel, robust, efficient machine learning algorithms tailored for automatic feature selection and early-stage heart disease detection. The proposed Catboost model achieves an F1-score of approximately 92.3% and an average accuracy of 90.94%. Compared to many current state-of-the-art approaches, it demonstrates superior classification performance with higher accuracy and precision.
Keywords: Heart disease, Machine learning, Feature selection, Cardiovascular diseases, Quality of life, Disease prevention, CVD
Edition: Volume 12 Issue 10, October 2023,
Pages: 2171 - 2182