The Advancements in Cardiovascular Health Surveillance: A Synthesis of Machine Learning Approaches
Keywords:
Machine learning, Cardiovascular Diseases, Support Vector Machines, Decision Trees, Logistic Regression, Random Forest, K-Nearest Neighbors, Neural Networks, Hybrid Models, Ensemble LearningAbstract
Cardiovascular diseases (CVDs) continue to stand as a prominent cause of morbidity and mortality worldwide. The timely identification and precise prediction of CVDs are critical for effective prevention and intervention strategies. In recent times, machine learning (ML) algorithms have emerged as potent instruments for assessing and predicting CVD risks. This paper undertakes a thorough review of the diverse applications of ML algorithms, encompassing Support Vector Machines (SVM), Decision Trees, Logistic Regression, Random Forest, and K- Nearest Neighbors (KNN), in detecting cardiovascular health issues. We explore the strengths and limitations inherent in each algorithm, investigate hybrid and ensemble learning approach, and delineate avenues for future research.
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Copyright (c) 2024 Praddyot Bodhankar, Swapnil Kumbhare, Shravani Kumbhare, Yash Kamone, Dr. D. W. Wajgi
This work is licensed under a Creative Commons Attribution 4.0 International License.
