A machine learning approach to predict stress hormones and inflammatory markers using illness perception and quality of life in breast cancer patients

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.