ArticleScientific reports2024
Factors influencing psychological distress among breast cancer survivors using machine learning techniques.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Frequency and Timing of Depression and Anxiety Diagnoses Following Cancer Diagnosis: A Multi-Center Cohort Study.Cancer medicine · 2026Article
- Gender role in radiotherapy: psychosocial differences between males and females during cancer care : Single-centre report on adult and paediatric oncological patients.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Evaluation of the Quality of Life of Women with Breast Cancer in Morocco.Annals of African medicine · 2026Article
- Analysis the incidence and related risk factors of depression in patients with esophageal cancer combined with bone metastasis.World journal of psychiatry · 2026Article
- Identifying psychosocial distress phenotypes in postoperative patients with breast cancer: A latent profile analysis.Asia-Pacific journal of oncology nursing · 2025Article
- An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals for Early Cancer Risk Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Quality of life in breast cancer survivors: An ambiguous loss perspective.Journal of family theory & review · 2025Article
- Impact of Depression and/or Anxiety on Mortality in Women with Gynecologic Cancers: A Nationwide Retrospective Cohort Study.Healthcare (Basel, Switzerland) · 2025Article
- Early Cancer Survivorship Distress Trajectories Associated With Socioeconomic Status and Age: Findings From a Multicenter Prospective Study.Cancer medicine · 2025Article
- Baseline Distress and Effectiveness of Survivor Video Narratives on Cancer-Associated Distress in Botswana: A Pilot Study.JCO global oncology · 2025Article
- The Mediating Effect of Emotional Regulation Between Psychological Resilience and Psychological Distress in Young and Middle-Aged Lymphoma Patients.Psychology research and behavior management · 2025Article
- Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data.Current oncology (Toronto, Ont.) · 2024Article
- The BCPM method: decoding breast cancer with machine learning.BMC medical imaging · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Five machine learning models were used to predict the classification of patients into mild and severe distress groups. The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors. Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.
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