Systematic misestimation of machine learning performance in neuroimaging studies of depression
Autor: | Daniel Emden, Xiaoyi Jiang, Tilo Kircher, David M. A. Mehler, Volker Arolt, Axel Krug, Ronny Redlich, Scott R. Clark, Tim Hahn, Ramona Leenings, Igor Nenadic, Simon B. Eickhoff, Udo Dannlowski, Bernhard T. Baune, Micah Cearns, Nils Opel, Claas Flint, Nils R. Winter |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Population Computer Science - Computer Vision and Pattern Recognition Neuroimaging Sample (statistics) Machine learning computer.software_genre Article Machine Learning FOS: Electrical engineering electronic engineering information engineering medicine Humans ddc:610 education Depression (differential diagnoses) Pharmacology Depressive Disorder Major education.field_of_study Depression business.industry Image and Video Processing (eess.IV) Diagnostic markers Small sample Electrical Engineering and Systems Science - Image and Video Processing Translational research Predictive analytics medicine.disease Magnetic Resonance Imaging Psychiatry and Mental health Sample size determination FOS: Biological sciences Quantitative Biology - Neurons and Cognition Major depressive disorder Neurons and Cognition (q-bio.NC) Artificial intelligence business Psychology computer |
Zdroj: | Neuropsychopharmacology Neuropsychopharmacology 46(8), 1510-1517 (2021). doi:10.1038/s41386-021-01020-7 |
ISSN: | 1740-634X 0893-133X |
DOI: | 10.1038/s41386-021-01020-7 |
Popis: | We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases. |
Databáze: | OpenAIRE |
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