Evaluating machine learning algorithms at predicting developmental trajectories using sequential dataset truncation of voluntary alcohol consumption in adolescent mice - Nathan Yu; Steven Buyske; Uthman Qureshi; Lei Yu (PLoS ONE, 2026)
Background Adolescent alcohol consumption is a known risk factor for developing alcohol use disorder (AUD) in adulthood, but individual susceptibility varies widely, contributed to by differences in factors that are not well-understood. Identifying patterns of developmental trajectories in voluntary alcohol consumption behavior during adolescence could provide insight into biological underpinnings of AUD risk. Machine learning (ML) offers powerful pattern recognition capabilities that may help forecast future behavioral trajectories based on early-stage data. Objective This study aimed to evaluate the performance of twelve supervised ML algorithms in predicting developmental trajectories of voluntary alcohol consumption behavior in adolescent mice using sequentially truncated datasets. Methods Simulated balanced datasets of alcohol consumption in adolescent mice were generated based on previously published biological data. We applied a sequential dataset truncation strategy to train and evaluate ML models on progressively longer spans of behavioral data. Prediction accuracy for trajectory pattern classification was assessed for each truncation point, and goodness-of-fit was modeled using four curve-fitting equations, including locally estimated scatterplot smoothing (LOESS), which provided best fit and was selected for downstream comparative analysis. Results LOESS-fitted accuracy progression curves enabled quantitative comparison across models. Six ML algorithms—Random Forest, Logistic Regression, Multilayer Perceptron, Linear Discriminant Analysis, K-Nearest Neighbors, and Support Vector Machine—achieved outstanding results, with 98% or better prediction accuracy by experiment end and 90% or better accuracy at midpoint. Four additional algorithms—Stochastic Gradient Descent, Decision Tree, Gradient Boosting Classifier, and Multinomial Naive Bayes—achieved acceptable accuracy values (77–95% at midpoint, and 91–96% at experiment end). In contrast, two models (Quadratic Discriminant Analysis and Gaussian Process Classifier) performed poorly and displayed declining accuracy trends with more data. Conclusions This study demonstrates that certain supervised ML algorithms can accurately predict behavioral outcomes from early-stage data. This approach holds promise for guiding molecular and cellular analyses at time points prior to behavioral phenotype’s fully manifesting, making it possible to identify potential biological drivers that initiate the onset of harmful behavior of alcohol consumption during adolescence development.