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Analysis of Machine Learning Models Predicting Quality of Life for Cancer Patients

Published: 09 November 2021 Publication History

Abstract

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. Two different approaches are employed for imputing missing values. 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.

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      cover image ACM Other conferences
      MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystems
      November 2021
      181 pages
      ISBN:9781450383141
      DOI:10.1145/3444757
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 09 November 2021

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      Author Tags

      1. Breast Cancer
      2. Cancer Patients
      3. Federated Learning
      4. Predictive Models
      5. Prostate Cancer
      6. Quality of Life

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      • European Union?s Horizon 2020 research and innovation programme

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      MEDES '21

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      Cited By

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      • (2025)Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIVIntegrated Pharmacy Research and Practice10.2147/IPRP.S492422Volume 14(1-16)Online publication date: Jan-2025
      • (2024)Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer PatientsJournal of Imaging10.3390/jimaging1012029710:12(297)Online publication date: 21-Nov-2024
      • (2024)A novel recommender framework with chatbot to stratify heart attack riskDiscover Medicine10.1007/s44337-024-00174-91:1Online publication date: 17-Dec-2024
      • (2023)The application of machine learning techniques in prediction of quality of life features for cancer patientsComputer Science and Information Systems10.2298/CSIS220227061S20:1(381-404)Online publication date: 2023
      • (2023)Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based AnalysisJMIR Public Health and Surveillance10.2196/452129(e45212)Online publication date: 24-Aug-2023
      • (2023)Aid of a machine learning algorithm can improve clinician predictions of patient quality of life during breast cancer treatmentsHealth and Technology10.1007/s12553-023-00733-713:2(229-244)Online publication date: 10-Feb-2023
      • (2023)The Role of Federated Learning in Processing Cancer Patients’ DataDevice-Edge-Cloud Continuum10.1007/978-3-031-42194-5_4(49-68)Online publication date: 10-Aug-2023
      • (2022)Successful Integration of EN/ISO 13606–Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a MethodologyJMIR Medical Informatics10.2196/4034410:10(e40344)Online publication date: 12-Oct-2022
      • (2022)Causal Inference for Personalized Treatment Effect Estimation for given Machine Learning Models2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00206(1289-1295)Online publication date: Dec-2022

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