Multiobjective Optimization of Interpretable Fuzzy Systems and Applicable Subjects for Fast Estimation of Obstructive Sleep Apnea-Hypopnea Severity

This article proposes an interpretable fuzzy estimation system (IFES) for fast estimation of severity of obstructive sleep apnea-hypopnea syndrome (OSAHS) using three easily available physiological variables: neck circumference, waist circumference, and average blood pressure after waking up. The IF...

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Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 31; H. 7; S. 2225 - 2237
Hauptverfasser: Juang, Chia-Feng, Pan, Guan-Ren, Huang, Wei-Chang, Wen, Chih-Yu, Wu, Ming-Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Apnea-hypopnea index (AHI)
Estimation
evolutionary fuzzy systems
Fuzzy neural networks
Fuzzy systems
Genetic algorithms
Hypertension
Indexes
interpretable AI
Medical diagnostic imaging
multiobjective optimization
Multiple objective analysis
obstructive sleep apnea
Optimization
Performance evaluation
Screening
Sleep apnea
ISSN:1063-6706, 1941-0034
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Zusammenfassung:This article proposes an interpretable fuzzy estimation system (IFES) for fast estimation of severity of obstructive sleep apnea-hypopnea syndrome (OSAHS) using three easily available physiological variables: neck circumference, waist circumference, and average blood pressure after waking up. The IFES aims to decrease the waiting list of polysomnography so that OSAHS treatment can be performed as soon as possible. The IFES is optimized to achieve the following three objectives: high estimation accuracy, the largest number of applicable subjects, and high model interpretability. The applicable subjects are determined by evaluating the influence of six screening factors, such as smoking, hypertension, and sleep efficiency, on the estimation performance. This article finds solutions of this multiobjective optimization problem using a multiobjective genetic algorithm. A total of 1197 participants are enrolled with the five-fold cross-validation scheme employed to evaluate an estimation performance. Experimental results show that the proposed method successfully finds the influence of different screening factors and the found IFESs outperform different estimation models used for comparison.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2022.3222033