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).
The researchers reported 24,970 incident of cardiovascular events (6.6%) occurrence. They found that among all the algorithms neural networks algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.
Compared to an established AHA/ACC risk prediction algorithm, we found all machine-learning algorithms tested were better at identifying individuals who will develop CVD and those that will not writes Wang. Unlike established approaches to risk prediction, the machine-learning methods used were not limited to a small set of risk factors, and incorporated more pre-existing medical conditions. Neural networks performed the best, with predictive accuracy improving by 3.6%. This is an encouraging step forward writes Wang.
