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Article

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

1
Department of Psychology, Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, 700554 Iași, Romania
2
Faculty of Medicine, Grigore T. Popa University, 700115 Iași, Romania
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2021, 28(4), 3150-3171; https://doi.org/10.3390/curroncol28040275
Received: 2 June 2021 / Revised: 15 August 2021 / Accepted: 17 August 2021 / Published: 19 August 2021
(This article belongs to the Special Issue Pathways to Psychological Resilience in Breast Cancer Survivorship)
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. View Full-Text
Keywords: stress hormones; inflammatory markers; breast cancer; machine learning stress hormones; inflammatory markers; breast cancer; machine learning
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MDPI and ACS Style

Crumpei-Tanasă, I.; Crumpei, I. A Machine Learning Approach to Predict Stress Hormones and Inflammatory Markers Using Illness Perception and Quality of Life in Breast Cancer Patients. Curr. Oncol. 2021, 28, 3150-3171. https://doi.org/10.3390/curroncol28040275

AMA Style

Crumpei-Tanasă I, Crumpei I. A Machine Learning Approach to Predict Stress Hormones and Inflammatory Markers Using Illness Perception and Quality of Life in Breast Cancer Patients. Current Oncology. 2021; 28(4):3150-3171. https://doi.org/10.3390/curroncol28040275

Chicago/Turabian Style

Crumpei-Tanasă, Irina, and Iulia Crumpei. 2021. "A Machine Learning Approach to Predict Stress Hormones and Inflammatory Markers Using Illness Perception and Quality of Life in Breast Cancer Patients" Current Oncology 28, no. 4: 3150-3171. https://doi.org/10.3390/curroncol28040275

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