[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121253-en":3,"doc-seo-121253-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121253,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Factors influencing psychological distress among breast cancer survivors using machine learning techniques","Breast cancer survivors experience significant distress stemming from physical, psychological, and social challenges that can persist after primary treatment. The study assessed distress severity in 641 adult participants using the National Comprehensive Cancer Network Distress Thermometer. Five machine learning models predicted mild versus severe distress. Results showed 57.7% reported severe distress, with top models highlighting depression plus partner-related, housing, work/school, and fatigue indicators as key predictors.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nFactors influencing psychological distress among breast cancer survivors using machine learning techniques  \nJin‑Hee Park1, Misun Chun2, Sun Hyoung Bae1, Jeonghee Woo3, EunaeChon3 & Hee Jun Kim4*  \nBreast 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  \nstudy 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.  \nKeywords Breast cancer, Distress, Machine learning, Quality of life, Distress thermometer  \nBreast cancer is the most common cancer among women worldwide, and South Korea is one of the Asian countries with the highest incidence of breast cancer1. The five-year survival rate for breast cancer in South Korea is currently 93.6%2. Unlike Europe and the United States, where breast cancer occurrence rates are high among women in their 50 s and 60 s, South Korea has a high proportion of women in their 40 s developing breast cancer. Therefore, helping breast cancer survivors to manage the breast cancer-related health problems that occur after primary treatment and enjoy a high quality of life is critical1,2.  \nBreast cancer patients experience distress as a result of various physical, psychological, and social problems that may arise during treatment3. Distress refers to an unpleasant experience that may be physical, mental, social, or spiritual in nature, which may hinder the ability of cancer patients to cope with treatment effectively4. The stress experienced by breast cancer patients varies in severity and incidence depending on the time of measurement5. However, it is the highest at the time when the cancer is diagnosed, and more than 30% of breast cancer patients experience severe stress even once treatment has been terminated or completed6,7. Temporary distress experienced by patients is a normal response; however, prolonged distress degrades their compliance and satisfaction with treatment8,9. Distress is also known to interfere with health-related decision making10 and decrease physical function, well-being, and quality of life5, 11, as well as resulting in negative effects throughout the course of cancer treatment4,9. Furthermore, distress among cancer patients, which is the sixth vital sign in cancer care, is a key predictor of cancer mortality and quality of life6. Therefore, its importance must be recognized in all processes of cancer diagnosis and treatment and it must be monitored, recorded, and managed continuously7, 12.  \n1College of Nursing, Research Institute of Nursing Science, Ajou University, Suwon, Republic of Korea. 2Department of Radiation Oncology, School of Medicine, Ajou University, Suwon, Republic of Korea. 3Mana","cbCaie2136JSlzMK","https://ap.wps.com/l/cbCaie2136JSlzMK","pdf",1220598,1,10,"English","en",105,"# Background and purpose\n## Distress definition and clinical importance\n## Rationale for machine learning\n# Study design and data\n## Participants and instrument\n## Features and factor identification\n# Predictive modeling results\n## Model performance and key indicators\n# Implications","[{\"question\":\"What was the main aim of the study?\",\"answer\":\"To measure psychological distress levels among breast cancer survivors and identify variables that significantly affect distress using machine learning models.\"},{\"question\":\"How was distress measured in the study?\",\"answer\":\"Distress was assessed using the National Comprehensive Cancer Network Distress Thermometer, based on responses from 641 adult breast cancer patients.\"},{\"question\":\"Which factors were most strongly associated with severe distress?\",\"answer\":\"Depression and indicators related to partner, housing, work/school, and fatigue were top predictors, and among emotional problems depression, fear, worry, loss of interest, and nervousness were significant.\"}]","Factors influencing psychological distress among breast cancer survivors using machine learning techniques | 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