Abstract
The application of machine learning in healthcare continues to gain attention as researchers attempt to prove its potential for the enhancement of diagnosis and prognosis accuracy. Although many applications of machine learning have been well studied, there remain substantial opportunities for advancement. The field of healthcare holds particularly strong potential for improvement from integration with machine learning. In the future, clinicians will likely utilize machine learning to enhance the efficiency of diagnosis and prognosis, optimizing the delivery of care. This study conducts a comprehensive examination of feature selection methodologies, model architectures, and fine-tuning techniques related to diverse diagnostic and prognostic scenarios within the domain of heart health. It addresses some key gaps in earlier research, including the lack of agreement on which data sources are most effective for classifying stroke and heart attack. This review contributes an analysis of current machine learning methods in stroke and heart attack research, highlighting key gaps such as limited use of multimodal data, external validation, and class imbalance mitigation. It suggests improvements, including the adoption of advanced sampling techniques and the use of comprehensive performance metrics. The findings suggest that despite extensive research on machine learning in cardiovascular health, there are gaps to be addressed in methodologies for data collection, preprocessing, model development, evaluation, and feature engineering.