Machine-based learning of multidimensional data in bipolar disorder – pilot results

Abstract

Introduction: Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates. Materials and Methods: To test this hypothesis, a de-identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms. Results: The best performing algorithm was logistic regression, with a macro-average F1-score of 0.69 [95% CI 0.66–0.73]. After further optimization, a model with an improved macro-average F1-score of 0.75, a micro-average F1-score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2-R test alone allow for a classification of bipolar disorder with equal performance. Conclusion: Using these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification.