Abstract
Impaired glucose tolerance (IGT) is associated with increased cardiovascular disease. Identifying IGT patients with rapid atherosclerosis progression will help in early risk-stratification. Machine learning (ML) involves algorithms that can learn complex relationships from data to make accurate predictions. Unlike traditional low-dimensional statistical approaches, ML can handle high dimensional datasets to make accurate predictions. Aim: To test the performance of ML in identifying IGT patients who will develop rapid carotid plaque progression. Methods: In the ACT NOW study (IGT patients were randomized to receive either pioglitazone or placebo), 382 subjects had carotid intima-media thickness (IMT) measured at baseline and at 15-18 months. Patients with IMT change in the top 10 percentile (n=39) were classified as rapid progressors (RP) and the rest (n=343) considered non-rapid progressors (NRP). ML using Naïve Bayes model was performed using 115 clinical/laboratory variables to develop a model to fit the true value of the class label (RP, NRP). Experiments were conducted with 10-fold cross validation in training/validation sets. Class prediction was also performed using traditional logistic regression model using 5 variables found different between RP and NRP by univariate analyses. Model performance was assessed using area under receiver operating characteristic curve. Results: Mean±SD IMT change was 0.011±0.026 mm overall; 0.058±0.018 for RP and 0.006±0.02 for NRP. Area under the curve (AUC) for ML (0.855) was higher versus traditional logistic regression model (0.73) (Fig.). ML correctly classified 78% of instances with κ statistic of 0.32. Conclusions: ML using naïve Bayes method provided good predictive ability in identifying IGT patients who had the most rapid plaque progression. ML may improve clinical prediction over traditional low-dimensional biostatistical approaches.
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