Apparently YES! Stephen F. Weng and colleagues from University of Nottingham, UK recently published a study that shows that machine-learning algorithms are better at predicting the absolute number of cardiovascular disease cases correctly, whilst successfully excluding non-cases. [Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12(4): e0174944. https://doi.org/10.1371/journal.pone.0174944]
Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. It has potential to transform medicine by better exploiting ‘big data’ for algorithm development writes Wang.
The researchers did a prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).