Background: Logistic regression (LR) is commonly used to estimate risk of coronary heart disease. We investigated if neural networks improved on the risk estimate of LR by analysing data from the Prospective Cardiovascular Münster Study (PROCAM), a large prospective epidemiological study of risk factors for coronary heart disease among men and women at work in northern Germany.
Methods: We used a multi-layer perceptron (MLP) and probabilistic neural networks (PNN) to estimate the risk of myocardial infarction or acute coronary death (coronary events) during 10 years' follow-up among 5159 men aged 35-65 years at recruitment into PROCAM. In all, 325 coronary events occurred in this group. We assessed the performance of each procedure by measuring the area under the receiver-operating characteristics curve (AUROC).
Results: The AUROC of the MLP was greater than that of the PNN (0.897 versus 0.872), and both exceeded the AUROC for LR of 0.840. If 'high risk' is defined as an event risk >20% in 10 years, LR classified 8.4% of men as high risk, 36.7% of whom suffered an event in 10 years (45.8% of all events). The MLP classified 7.9% as high risk, 64.0% of whom suffered an event (74.5% of all events), while with the PNN, only 3.9% were at high risk, 58.6% of whom suffered an event (33.5% of all events).
Conclusion: Intervention trials indicate that about one in three coronary events can be prevented by 5 years of lipid-lowering treatment. Our analysis suggests that use of the MLP to identify high-risk individuals as candidates for drug treatment would allow prevention of 25% of coronary events in middle-aged men, compared to 15% and 11% with LR and the PNN, respectively.