Integrating Machine Learning with Clinical Practice: Advancements in Heart Disease Prediction Models

Heart disease continues to be one of the leading causes of death worldwide. Despite significant advancements in medical technology, cardiovascular diseases remain a major health concern. Early detection and accurate prediction of heart disease are essential for improving patient outcomes and reducing healthcare costs. This paper explores how machine learning (ML) models can enhance heart disease prediction and support clinical decision-making. The study provides an indepth review of current ML models, including logistic regression, decision trees, random forests, support vector machines (SVM), and deep learning techniques, such as neural networks. Heart disease is still one of the major killers in the world, and it will be important to create predictive models that can enable doctors to conduct early diagnosis and intervention. This paper explores how machine learning methods can be combined with clinical practice to enhance prediction in heart disease. We propose a detailed review of current methodologies in ML methods which have been utilized in predicting heart disease including logistic regression, decision trees, random forests, support vector machines, and neural networks. The study concentrates on the performance of the models built using clinical datasets, incorporating features of patient demographics, medical history, and laboratory results. It addresses issues of model accuracy, data quality, and the lack of interpretability inherent in models. The article establishes a comparison between the traditional clinical prediction methods and newer advanced ML models, improving predictive performance and providing avenues for clinical integration. Finally, it suggests a framework for ML based predictions in clinical workflows by exploiting the two key features: real-time risk assessment and personalization of treatment plans. Our results show that ML models, specifically ensemble and deep learning techniques, offer significant improvements over traditional approaches to heart disease prediction problems.