Predicting Quality of Life for Breast Cancer Patients

Christos Raspoptsis, Eugenia Mylona, Konstantina Kourou, Georgios Manikis, Haridimos Kondylakis*, Kostas Marias, Paula Poikonen-Saksela, Panagiotis Simos, Evangelos Karademas, Ketti Mazzocco, Ruth Pat-Horenczyk, Berta Sousa, Dimitrios I. Fotiadis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The diagnosis of breast cancer has a significant impact on a patient's quality of life. Several demographic and clinical factors have been reported to affect the quality of life of breast cancer patients. However, few studies have a sufficient sample size for multifactorial assays to be tested. In the present work, we explore a rich set of clinical, psychological, socio-demographic, and lifestyle data from a large multicenter study of breast cancer patients (n = 765), with the aim to predict their global quality of life (QoL) 18 months after the diagnosis and to identify possible QoL-related prognostic factors. For QoL prediction, a set of Machine Learning methods were explored, namely Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Depending on the model used, prediction accuracy varied between 0.305 and 0.864. Across models, a largely common set of psychological characteristics (optimism, perceived ability to deal with trauma, resilience as a trait, ability to understand the disease), as well as subjective perceptions of personal functionality (physical, social, cognitive function), were identified as key prognostic factors of long-term quality of life after a breast cancer diagnosis.Clinical Relevance - Early detection of protective and obstructive factors associated with patient well-being can help health professionals to tailor preventive psychological programs aimed at enhancing the ability of breast cancer patients to adapt effectively to the disease.

Original languageAmerican English
Title of host publicationBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9798350310504
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2023 - Pittsburgh, United States
Duration: 15 Oct 202318 Oct 2023

Publication series

NameBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings

Conference

Conference2023 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2023
Country/TerritoryUnited States
CityPittsburgh
Period15/10/2318/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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