Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis. (April 2022) - Colombo, Federica; Calesella, Federico; Mazza, Mario Gennaro; Melloni, Elisa Maria Teresa; Morelli, Marco J.; Scotti, Giulia Maria; Benedetti, Francesco; Bollettini, Irene; Vai, Benedetta
Abstract: Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11, 336 participants, 3903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p = 0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies. Highlights: We meta-analysed machine learning studies classifying bipolar disorder. A wide range of potential markers of bipolar disorder was considered. The overall pooled classification accuracy was 0.77. Machine learning algorithms should be defined according to the specific marker. Better methodological practices are recommended to improve classification accuracy.