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

Mahfuz Ahmed Anik, Mohsin Mahmud Topu, Azmine Toushik Wasi, Md Isfar Khan, MD Manjurul Ahsan

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.

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

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.
Paper Structure (47 sections, 13 equations, 6 figures, 8 tables)

This paper contains 47 sections, 13 equations, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Sleep Quality Measurement and Predictive Modelling
  4. Explainable AI for Sleep and Health Applications
  5. From Prediction to Prescription: ML-Informed optimization and Decision Support
  6. Research Gap Analysis
  7. Data and Predictive Modeling
  8. Dataset Description and Preprocessing
  9. Survey design
  10. Data Preprocessing and Feature Engineering
  11. Exploratory Data Analysis.
  12. Actionable and Non-Actionable Variables
  13. Sleep Quality Prediction and Explainability
  14. Benchmarking of Machine Learning Models
  15. XGBoost Configuration and Tuning
  16. ...and 32 more sections

Figures (6)

  • Figure 1: Generic sleep advice imposes high behavioral burden under real-world constraints, frequently resulting in poor follow-through and disrupted sleep. A minimal-change optimization framework instead delivers personalized, low-effort actions that promote more sustainable sleep improvement.
  • Figure 2: (a) Class distribution of sleep quality; (b) Sample counts for each sleep quality class after data augmentation.
  • Figure 3: Pearson correlation matrix of sleep quality and associated features
  • Figure 4: Mean absolute SHAP values for the XGBoost sleep quality prediction model. Higher values indicate features with greater influence on model predictions.
  • Figure 5: Overview of the proposed predictive–prescriptive framework. Survey data are preprocessed and used to train an XGBoost model for sleep quality prediction. SHAP-based analysis derives importance weights for actionable variables, which are incorporated into a constrained mixed-integer linear programming (MILP) model. The optimization produces personalized, feasible, and minimally disruptive sleep improvement recommendations.
  • ...and 1 more figures