An Adaptive Approach to Heart Disease Prediction: A Review of Machine Learning Techniques

An Adaptive Approach to Heart Disease Prediction: A Review of Machine Learning Techniques

Risha Ahmed1,Dr. Anurag jain2

1 Research scholar, Computer Science & Engineering, REC, Bhopal, M.P

2 Director Computer Science & Engineering, Radharamangroup , Bhopal, M.P

Abstract:

Heart disease remains a significant public health concern, emphasizing the need for accurate and timely predictive models to guide preventive interventions. This review explores the application of adaptive machine learning techniques in predicting heart disease, focusing on their ability to continuously update and improve prediction accuracy over time. Various adaptive algorithms, including online learning, reinforcement learning, transfer learning, and ensemble methods, are discussed along with their applications in clinical practice, remote monitoring, and population health management. The review also highlights challenges such as data heterogeneity, model interpretability, and ethical considerations, proposing future research directions to address these issues. By harnessing the power of adaptive machine learning, healthcare providers can enhance early detection, personalize interventions, and ultimately reduce the burden of heart disease on society.

Keywords:

Data Mining, Machine Learning, Heart Disease, Classification, Prediction, Healthcare informatics, Classification, Medical decision support system