The application of machine learning techniques in prediction of quality of life features for cancer patients

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).