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Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs. [ABSTRACT FROM AUTHOR]

The article focuses on gaining publications is of paramount importance to many junior doctors and medical students alike, often intense and challenging years of learning and training undertaken, many trainees feel the lingering need to do more to advance their future career prospects by publishing. Topics include the case from final year of medical school to more senior training, and the great source of anxiety for many trainees who on top of their heavy workloads on the ward.