Prediction of high-level fear of cancer recurrence in breast cancer survivors: An integrative approach utilizing random forest algorithm and visual nomogram.
This study is the first attempt to use a combination of regression analysis and random forest algorithm to predict the risk factors for high-level fear of cancer recurrence and develop a predictive nomogram to guide clinicians and nurses in identifying high-risk populations for high-level fear of ca...
| Published in: | European Journal of Oncology Nursing Vol. 70 |
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| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
Churchill Livingstone, Inc.
Jun2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177655330&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177655330 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14623889 8IF jtl: European Journal of Oncology Nursing issn: 14623889 maglogo: N pubinfo: dt: Jun2024 vid: 70 pid: 1242 pub: Churchill Livingstone, Inc. artinfo: ui: 177655330 177655330 177655330 10.1016/j.ejon.2024.102579 177655330 ppct: 1 formats: tig: atl: Prediction of high-level fear of cancer recurrence in breast cancer survivors: An integrative approach utilizing random forest algorithm and visual nomogram. aug: au: Ren, Hui Yang, Tianye Yin, Xin Tong, Lingling Shi, Jianjun Yang, Jia Zhu, Zhu Li, Hongyan affil: Nursing Department, The First Hospital of Jilin University, Changchun, Jilin Province, China sug: subj: Breast Neoplasms Surgery Neoplasm Recurrence, Local Psychosocial Factors Cancer Survivors Psychosocial Factors Fear Risk Assessment Algorithms Utilization Prediction Models Models, Statistical Theory Construction Health Personnel Psychosocial Factors Oncology Nurses Psychosocial Factors Human Attitude to Illness Regression Research Subject Recruitment China Cognition Social Factors Economic Factors Reliability and Validity Psychology Univariate Statistics Multiple Regression Emotional Regulation ROC Curve Credibility (Research) ab: This study is the first attempt to use a combination of regression analysis and random forest algorithm to predict the risk factors for high-level fear of cancer recurrence and develop a predictive nomogram to guide clinicians and nurses in identifying high-risk populations for high-level fear of cancer recurrence. After receiving various recruitment strategies, a total of 781 survivors who had undergone breast cancer resection within 5 years in four Grade-A hospitals in China were included. Besides demographic and clinical characteristics, variables were also selected from the perspectives of somatic, cognitive, psychological, social and economic factors, all of which were measured using a scale with high reliability and validity. This study established univariate regression analysis and random forest model to screen for risk factors for high-level fear of cancer recurrence. Based on the results of the multi-variable regression model, a nomogram was constructed to visualize risk prediction. Fatigue, social constraints, maladaptive cognitive emotion regulation strategies, meta-cognition and age were identified as risk factors. Based on the predictive model, a nomogram was constructed, and the area under the curve was 0.949, indicating strong discrimination and calibration. The integration of two models enhances the credibility of the prediction outcomes. The nomogram effectively transformed intricate regression equations into a visual representation, enhancing the readability and accessibility of the prediction model's results. It aids clinicians and nurses in swiftly and precisely identifying high-risk individuals for high-level fear of cancer recurrence, enabling the development of timely, predictable, and personalized intervention programs for high-risk patients. • Exploring modifiable risk factors will provide great opportunities for the prevention and intervention of FCR. • Random forest can provide a simple visual method to accurately rank the importance of related factors. • Nomogram accurately converted complex regression equations into visual graphics, making the prediction model more readable. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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