Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions.

ABSTRACT: Artificial intelligence promises to revolutionize mental health care, but small dataset sizes and lack of robust methods raise concerns about result generalizability. To provide insights on minimal necessary data set sizes, we explore domain-specific learning curves for digital intervention dropout predictions based on 3654 users from a single study (ISRCTN13716228, 26/02/2016). Prediction performance is analyzed based on dataset size (N = 100-3654), feature groups (F = 2-129), and algorithm choice (from Naive Bayes to Neural Networks). The results substantiate the concern that small datasets (N ≤ 300) overestimate predictive power. For uninformative feature groups, in-sample prediction performance was negatively correlated with dataset size. Sophisticated models overfitted in small datase

SUBMITTER: Zantvoort K 

PROVIDER: S-EPMC11655521 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature