Data-driven predictive models using machine learning techniques can significantly enhance early detection accuracy and reduce diagnostic delays in cardiovascular healthcare.
Do machine learning-based classification techniques improve the early detection accuracy of cardiovascular disease compared to traditional diagnostic approaches?
Machine learning and data mining techniques offer promising tools to enhance the early detection and diagnostic accuracy of cardiovascular diseases.
Cardiovascular disease is one of the leading causes of mortality worldwide, accounting for approximately 17.9 million deaths annually, representing about 15% of global deaths. Early detection of cardiovascular conditions is critical, as timely diagnosis can significantly reduce complications and improve patient survival rates. Traditional diagnostic approaches often face limitations in handling large-scale patient data and may struggle with timely and accurate detection, especially in cases involving multiple comorbidities. With advancements in computational intelligence, machine learning and data mining techniques have emerged as powerful tools for disease prediction and classification in healthcare. This study explores the application of various machine learning-based classification techniques, including Naïve Bayes, Support Vector Machine, Decision Tree, k-Nearest Neighbor, Artificial Neural Networks, and hybrid intelligent systems, for early prediction of cardiovascular disease. These methods enable efficient analysis of key health parameters such as blood pressure, cholesterol level, heart rate, and glucose level to support accurate diagnostic decision-making. The findings highlight that data-driven predictive models can significantly enhance early detection accuracy, reduce diagnostic delays, and support clinical decision-making in cardiovascular healthcare
Reddy Venkata Sai Kumar (Wed,) conducted a review in Cardiovascular disease. Machine learning-based classification techniques vs. Traditional diagnostic approaches was evaluated on Early detection accuracy. Data-driven predictive models using machine learning techniques can significantly enhance early detection accuracy and reduce diagnostic delays in cardiovascular healthcare.