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

Milovs Savic; Vladimir Kurbalija; Mihailo Ilic; Mirjana Ivanovic; Duvsan Jakovetic; Antonios Valachis; Serge Autexier; Johannes Rust; Thanos Kosmidis
In: Mirjana Ivanovic; Zakaria Maamar (Hrsg.). Proceedings of the 13th International Conference on Management of Digital EcoSystems. The International ACM Conference on Management of Emergent Digital EcoSystems (MEDES-2021), November 1-3, Virtual Event, Tunisia, MEDES '21, ISBN 9781450383141, Association for Computing Machinery, 11/2021.


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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