Abstract
Machine learning offers potential to enhance cardiovascular diagnostics by analyzing various types of data, including but not limited to medical imaging. However, selecting the appropriate ML algorithm for predicting specific cardiovascular diseases is complex and depends on factors such as the type of data, the imaging modality, and the characteristics of the disease. This review aims to provide guidance on selecting suitable machine learning algorithms for diagnosing and predicting specific cardiovascular conditions using various medical imaging modalities. Recent studies were reviewed, focusing on machine learning algorithms applied to cardiovascular imaging for the classification and prediction of cardiovascular diseases. Performance metrics such as accuracy and area under the curve were considered to evaluate the models. A summary table was created to compare the effectiveness of different machine learning algorithms across various cardiovascular conditions and imaging techniques. The review demonstrates that different machine learning algorithms have unique strengths depending on the imaging modality and the specific cardiovascular disease. For example, convolutional neural networks are effective for processing image data like echocardiograms, while support vector machines and random forests are suitable for structured, tabular data. Many studies lack external validation and have issues such as data leakage, raising concerns about the generalizability of their results. Guidelines are provided to help clinicians and researchers select the most appropriate machine learning model based on the medical condition and imaging modality. Additionally, the importance of proper evaluation of machine learning studies is emphasized, including data splitting strategies, learning curves, and validation methods, to ensure the reliability of machine learning models in cardiovascular diagnostics. Selecting the appropriate machine learning algorithm for cardiovascular diagnostics is important and should be guided by the specific disease, imaging modality, and data characteristics. Robust evaluation and validation of machine learning models are essential to ensure their generalizability and clinical utility. Collaboration between medical professionals and machine learning experts is necessary to develop transparent, robust models that can improve patient outcomes in cardiovascular care.