Integrating Explainable Machine Learning and Mixed-Integer Optimization for Personalized Sleep Quality Intervention

2026-03-14

AI Summary

This study addresses the limitation of existing sleep quality research, which predominantly focuses on prediction while lacking actionable, personalized intervention strategies. To bridge this gap, the authors propose an end-to-end prediction-prescription framework that uniquely integrates interpretable machine learning—using SHAP feature attribution—with mixed-integer optimization. Building upon a high-accuracy predictive model (F1 = 0.9544, accuracy = 0.9366), the framework generates minimal, feasible, and high-impact intervention recommendations—typically only one or two actions—while explicitly accounting for individual resistance to behavioral change. Robustness of the prescribed interventions is ensured through Pareto and sensitivity analyses, effectively translating predictive insights into low-burden, structured, and personalized guidance for improving sleep quality.

Technology Category

• Machine Learning: Privacy
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sleep quality is influenced by a complex interplay of behavioral, environmental, and psychosocial factors, yet most computational studies focus mainly on predictive risk identification rather than actionable intervention design. Although machine learning models can accurately predict subjective sleep outcomes, they rarely translate predictive insights into practical intervention strategies. To address this gap, we propose a personalized predictive-prescriptive framework that integrates interpretable machine learning with mixed-integer optimization. A supervised classifier trained on survey data predicts sleep quality, while SHAP-based feature attribution quantifies the influence of modifiable factors. These importance measures are incorporated into a mixed-integer optimization model that identifies minimal and feasible behavioral adjustments, while modelling resistance to change through a penalty mechanism. The framework achieves strong predictive performance, with a test F1-score of 0.9544 and an accuracy of 0.9366. Sensitivity and Pareto analyses reveal a clear trade-off between expected improvement and intervention intensity, with diminishing returns as additional changes are introduced. At the individual level, the model generates concise recommendations, often suggesting one or two high-impact behavioral adjustments and sometimes recommending no change when expected gains are minimal. By integrating prediction, explanation, and constrained optimization, this framework demonstrates how data-driven insights can be translated into structured and personalized decision support for sleep improvement.

Problem

• sleep quality
• personalized intervention
• actionable recommendations
• behavioral adjustment
• predictive modeling

Innovation

• Explainable Machine Learning
• Mixed-Integer Optimization
• Personalized Intervention
• SHAP
• Behavioral Recommendation