The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients

Miloš Savić1, Vladimir Kurbalija1, Mihailo Ilić1, Mirjana Ivanović1, Dušan Jakovetić1, Antonios Valachis2, Serge Autexier3, Johannes Rust3 and Thanos Kosmidis4

  1. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad
    Trg D. Obradovića 3, Novi Sad, Serbia
    {svc, kurba, milic, mira, dusan.jakovetic}
  2. Department of Oncology, Faculty of Medicine and Health, Örebro University
    SE 70182 Örebro, Sweden
  3. German Research Center for Artificial Intelligence GmbH, Cyber-Physical Systems
    Bremen, Germany
    {serge.autexier, johannes.rust}
  4. Care Across Ltd
    London, England


Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL information it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy preserving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of cancer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differential privacy).

Key words

Quality of Life, Cancer Patients, Predictive Models, Federated Learning, Breast Cancer, Prostate Cancer

Digital Object Identifier (DOI)

Publication information

Volume 20, Issue 1 (January 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Savić, M., Kurbalija, V., Ilić, M., Ivanović, M., Jakovetić, D., Valachis, A., Autexier, S., Rust, J., Kosmidis, T.: The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients. Computer Science and Information Systems, Vol. 20, No. 1, 381–404. (2023),