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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.<\/jats:p>","DOI":"10.1038\/s41598-024-65132-y","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T01:26:19Z","timestamp":1719883579000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Factors influencing psychological distress among breast cancer survivors using machine learning techniques"],"prefix":"10.1038","volume":"14","author":[{"given":"Jin-Hee","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Misun","family":"Chun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun Hyoung","family":"Bae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeonghee","family":"Woo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunae","family":"Chon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Jun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"65132_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. 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