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CC BY-NC-SA V4.0, Open Access
Thesis
CONFORMALIZED UNCERTAINTY REGIONS FOR MACHINE LEARNING-BASED MULTIPLE COGNITIVE HEALTH MEASURES FROM SMART WATCH SENSOR DATA
Washington State University
Master of Science (MS), Washington State University
05/2025
DOI:
https://doi.org/10.7273/000007486
Abstract
Machine learning (ML) algorithms play an increasingly critical role in remote health monitoring, automating health assessments, and extending medical professionals’ reach in
caring for an aging population. However, ML models often provide predictions without quantifying uncertainty, which is essential for safe deployment in healthcare. This paper
presents a novel method referred to as Uncertainty Regions via Importance-weighted Calibration (URIC) for automating the uncertainty-based prediction of multiple clinical cognitive health measures from continuous smartwatch sensor data. URIC leverages Conformal Prediction (CP), a rigorous uncertainty quantification (UQ) framework that guarantees user-defined coverage (e.g., ground truth is in the predicted region with 95% probability). The key innovation of URIC is constructing smaller, more interpretable prediction regions by assigning importance weights to calibration examples. It forms prediction regions by using the most relevant calibration examples, ensuring tight regions without sacrificing coverage. We evaluate URIC for predicting the cognitive health measures of 157 adults who were either cognitively healthy or experienced mild cognitive impairment (MCI). Compared to other UQ methods, our method constructs smaller prediction regions across all multi-target combinations while maintaining the user-specified coverage. This approach can be integrated into human-ML collaborative systems to improve diagnostic accuracy and interpretability in sensitive multi-target healthcare tasks.
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Details
- Title
- CONFORMALIZED UNCERTAINTY REGIONS FOR MACHINE LEARNING-BASED MULTIPLE COGNITIVE HEALTH MEASURES FROM SMART WATCH SENSOR DATA
- Creators
- Chibuike Emmanuel Ugwu
- Contributors
- Janardhan Rao Doppa (Co-Chair)Yan Yan (Co-Chair)Ganapati Bhat (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- School of Electrical Engineering and Computer Science
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 66
- Identifiers
- 99901220447201842
- Language
- English
- Resource Type
- Thesis