Towards a Machine Learning Model for Cognitive Performance Prediction

Laura Ginestretti, Jacopo Lazzari, Alessandro Verosimile, Susanna Bardini, Marco Santambrogio

Confirmation: I have read and agree with the IEEE BHI 2025 conference submission's policy on behalf of myself and my co-authors.

Keywords: Cognitive Performance Prediction; Explainable Machine Learning; Personalized Health Monitoring; Counterfac- tual Analysis

Abstract:

Predicting cognitive performance by leveraging
physiological, lifestyle, and demographic data is vital for advancing personalized mental health management and improving
individual outcomes. This study presents an explainable regression framework designed to predict cognitive performance while
providing meaningful and interpretable insights aimed at its
improvement. To capture both nonlinear and individual-specific
relationships present in multimodal datasets, we utilize machine
learning models such as XGBoost and Linear Generalized Additive Models (GAM). In this scenario, interpretability plays a
central role in translating machine learning model predictions
into actionable knowledge for cognitive performance improvement. To this end, we employ SHapley Additive Explanations
(SHAP) for global and local feature attribution, which reveals
how factors such as physical activity, respiration patterns, and
heart rate variability impact cognitive outcomes at individual
level. Moreover, Diverse Counterfactual Explanations (DiCE) are
used to generate multiple plausible and realistic modifications to
input features, giving the possibility to elaborate personalized
recommendations that can improve predicted cognitive performance. The proposed framework is validated on two distinct
datasets: the first one comprising real-life oral presentations
evaluated by external experts, where XGBoost achieved a 42.5%
improvement over baseline; the second one based on standardized computerized cognitive assessments, where Linear GAMs
reached a 46.8% improvement. These results demonstrate the
benefits of combining strong predictive accuracy with interpretability, supporting tailored cognitive health monitoring and
intervention planning. Overall, this work highlights the benefits of
multifactorial, explainable machine learning models to optimize
individual cognitive performance through personalized, data-driven strategies.

Track: 7. General Track

Registration Id: 6KNLPLXTX98

Submission Number: 212