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A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea

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Abstract

A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess the utility of two important machine-learning techniques to classify subjects with obstructive sleep apnea (OSA) using their daytime tracheal breathing sounds. We evaluate and compare the performance of the random forest (RF) and regularized logistic regression (LR) as feature selection tools and classification approaches for wakefulness OSA screening. Results show that the RF, which is a low-variance committee-based approach, outperforms the regularized LR in terms of blind-testing accuracy, specificity, and sensitivity with 3.5%, 2.4%, and 3.7% improvement, respectively. However, the regularized LR was found to be faster than the RF and resulted in a more parsimonious model. Consequently, both the RF and regularized LR feature reduction and classification approaches are qualified to be applied for the daytime OSA screening studies, depending on the nature of data and applications’ purposes.

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Abbreviations

AHI:

Apnea-Hypopnea Index

ANOVA:

Analysis of variance

AUC:

Area under the curve

CI:

Confidence interval

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

MANOVA:

Multivariate analysis of variance

NC:

Neck circumference

OSA:

Obstructive sleep apnea

OOB:

Out-of-bag

PSD:

Power spectrum density

PSG:

Polysomnography

ROC:

Receiver operating characteristics

RF:

Random forest

TBS:

Tracheal breathing sounds

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Authors and Affiliations

  1. Biomedical Engineering Program, University of Manitoba, Winnipeg, Canada

    Farahnaz Hajipour & Zahra Moussavi

  2. Department of Statistics, University of Manitoba, Winnipeg, Canada

    Mohammad Jafari Jozani

  3. Electrical and Computer Engineering Department, University of Manitoba, Winnipeg, Canada

    Zahra Moussavi

Authors
  1. Farahnaz Hajipour
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  2. Mohammad Jafari Jozani
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  3. Zahra Moussavi
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Correspondence to Farahnaz Hajipour.

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The study was approved by the Biomedical Research Ethics Board of the University of Manitoba. All participants signed informed consent before data collection.

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Hajipour, F., Jozani, M.J. & Moussavi, Z. A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea. Med Biol Eng Comput 58, 2517–2529 (2020). https://doi.org/10.1007/s11517-020-02206-9

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  • DOI: https://doi.org/10.1007/s11517-020-02206-9

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