Explainable machine learning analysis of longitudinal mental health trajectories after breast cancer diagnosis

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

*Corresponding author for this work

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

Abstract

Mental health impairment after breast cancer diagnosis may persist for months or years. The present work leverages on novel machine learning techniques to identify distinct trajectories of mental health progression in an 18-month period following BC diagnosis and develop an explainable predictive model of mental health progression using a large list of clinical, sociodemographic and psychological variables. The modelling process was conducted in two phases. The first modeling step included an unsupervised clustering to define the number of trajectory clusters, by means of a longitudinal K-means algorithm. In the second modeling step an explainable ML framework was developed, on the basis of Extreme Gradient Boosting (XGBoost) model and SHAP values, in order to identify the most prominent variables that can discriminate between good and unfavorable mental health progression and to explain how they contribute to model's decisions. The trajectory analysis revealed 5 distinct trajectory groups with the majority of patients following stable good (56%) or improving (21%) trends, while for others mental health levels either deteriorated (12%) or remained at unsatisfactory levels (11%). The model's performance for classifying patient mental health into good and unfavorable progression achieved an AUC of 0.82 - 0.04. The top ranking predictors driving the classification task were the higher number of sick leave days, aggressive cancer type (triple-negative) and higher levels of negative affect, anxious preoccupation, helplessness, arm and breast symptoms, as well as lower values of optimism, social and emotional support and lower age.

Original languageAmerican English
Title of host publicationBHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781665487917
DOIs
StatePublished - 2022
Event2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022 - Ioannina, Greece
Duration: 27 Sep 202230 Sep 2022

Publication series

NameBHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings

Conference

Conference2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022
Country/TerritoryGreece
CityIoannina
Period27/09/2230/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • K-means clustering
  • breast cancer
  • interpretability
  • mental health
  • trajectory analysis

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