Application of machine learning methods for the prediction of distress in patients with oncological diseases

Abstract

Distress management is of particular importance in all disease treatment strategies that aim to cope with medical conditions, which require prolonged therapy. Here, we present results obtained in a comparative study of various classification methods for automated distress detection. For the purposes of the present study, use was made of a common experimental protocol that relies on a dataset of approximately 6 000 oncological patients at different stages of therapy. The dataset consists of the binary responses to specific questions in a purposefully-designed self-evaluation questionnaire on the degree of distress. Conducted, within such a framework, was a performance assessment of three distress detectors based on Multilayer Perceptron Neural Network (MLP NN), boosting and bagging meta-classification methods and evaluated, further, was the performance of nine characteristic descriptors (KR1-KR9) representing the informative content of the dataset in different ways. The results obtained in the experiments prove conclusively that one of the characteristic descriptors, KR8 and KR9, significantly outperform the other descriptors in terms of classification accuracy, precision, recall, and F-measure.