A Machine Learning Approach to Predict Stress Hormones and Inflammatory Markers Using Illness Perception and Quality of Life in Breast Cancer Patients

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ID: 282842
2021
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Abstract
Psychosocial factors have become central concepts in oncology research. However, their role in the prognosis of the disease is not yet well established. Studies on this subject report contradictory findings. We examine if illness perception and quality of life reports measured at baseline could predict the stress hormones and inflammatory markers in breast cancer survivors, one year later. We use statistics and machine learning methods to analyze our data and find the best prediction model. Patients with stage I to III breast cancer (N = 70) were assessed twice, at baseline and one year later, and completed scales assessing quality of life and illness perception. Blood and urine samples were obtained to measure stress hormones (cortisol and adrenocorticotropic hormone (ACTH) and inflammatory markers (c-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and fibrinogen). Family quality of life is a strong predictor for ACTH. Women who perceive their illness as being more chronic at baseline have higher ESR and fibrinogen values one year later. The artificial intelligence (AI) data analysis yields the highest prediction score of 81.2% for the ACTH stress hormone, and 70% for the inflammatory marker ESR. A chronic timeline, illness control, health and family quality of life were important features associated with the best predictive results.
Reference Key
crumpei-tanasă2021a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Crumpei-Tanasă, Irina; Crumpei, Iulia
Journal current oncology (toronto, ont)
Year 2021
DOI
10.3390/curroncol28040275
URL
https://doi.org/10.3390/curroncol28040275
Keywords
Cancer quality perception stress learning patients life hormones breast markers approach illness inflammatory using machine predict

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