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Background <p>Sleep disturbance is a common physical and mental disease, which has a high incidence in breast cancer population and has a great impact on patients treatment outcomes. This study aimed to construct a predictive model for sleep disturbance in breast cancer using machine learning (ML) algorithms and identify the most vital risk predictors.</p> Methods <p>Through cross-sectional stratified random sampling, 644 breast cancer patients completed a face-to-face interview questionnaire. Sleep disturbances were assessed using the Patient-Reported Outcomes Measurement Information System Sleep Disturbance 8-item short form. Based on the Maximum Relevant and Minimum Redundancy (MRMR) method, the importance of 26 predictive factors was ranked. Four ML algorithms were used to develop models that predict sleep disturbances in breast cancer.</p> Results <p>The prevalence of sleep disturbance among breast cancer was 30.59%. The four ML models showed good prediction performances in the test set (area under the receiver operating characteristic curve: 0.74–0.83; accuracy: 0.73–0.82). The features strongly associated with sleep disturbance were loneliness, new possibilities of post-traumatic growth, anxiety, depression, and social support.</p> Conclusions <p>ML can effectively predict sleep disturbances in breast cancer. Interventions targeting on the reduction of loneliness, anxiety and depression and enhancement of post-traumatic growth (new possibilities) and social support are needed to alleviate sleep disturbances.</p>

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Predicting sleep disturbance among patients with breast cancer in China through machine learning algorithms-a multi-site survey study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Chong Liu,
  • Shu Li,
  • Xiaofang Zhao,
  • Lingfang Ning,
  • Cong Li,
  • Xiaoshi Yang

摘要

Background

Sleep disturbance is a common physical and mental disease, which has a high incidence in breast cancer population and has a great impact on patients treatment outcomes. This study aimed to construct a predictive model for sleep disturbance in breast cancer using machine learning (ML) algorithms and identify the most vital risk predictors.

Methods

Through cross-sectional stratified random sampling, 644 breast cancer patients completed a face-to-face interview questionnaire. Sleep disturbances were assessed using the Patient-Reported Outcomes Measurement Information System Sleep Disturbance 8-item short form. Based on the Maximum Relevant and Minimum Redundancy (MRMR) method, the importance of 26 predictive factors was ranked. Four ML algorithms were used to develop models that predict sleep disturbances in breast cancer.

Results

The prevalence of sleep disturbance among breast cancer was 30.59%. The four ML models showed good prediction performances in the test set (area under the receiver operating characteristic curve: 0.74–0.83; accuracy: 0.73–0.82). The features strongly associated with sleep disturbance were loneliness, new possibilities of post-traumatic growth, anxiety, depression, and social support.

Conclusions

ML can effectively predict sleep disturbances in breast cancer. Interventions targeting on the reduction of loneliness, anxiety and depression and enhancement of post-traumatic growth (new possibilities) and social support are needed to alleviate sleep disturbances.