Advancing Heart Disease Prediction: A Hybrid Approach Integrating Machine Learning and Clinical Expertise

Globally, cardiovascular diseases (CVDs) remain the primary cause of illness and deaths. Healthcare providers must be able to anticipate the onset of these diseases in order to make well-informed decisions about the diagnosis, treatment, and care of their patients. Thus, Machine learning (ML) models have become powerful tools in this context by offering the potential to identify critical risk factors and enhance the diagnostic process related to heart disease. The primary objective of this work is to leverage machine learning techniques to improve the prediction accuracy of heart disease. By identifying the most significant features through feature selection techniques, the study aims to enhance the predictive capability of the models. Furthermore, the research develops the possibility of building a hybrid model by merging the top two models based on their evaluation metrics. By using this hybrid model, the prediction of cardiovascular diseases should be greatly enhanced. Healthcare professionals can potentially avert the onset of severe cardiac events by appropriately identifying patients who are at high risk and taking preventative action. Additionally, this work adds to the ongoing efforts to create trustworthy and accurate algorithms for the prediction of cardiovascular disease, which is crucial for bettering patient outcomes and lowering the prevalence of heart disease worldwide. This method advances preventive healthcare and personalized medicine, which is a larger objective, in addition to increasing prediction accuracy.