Physical Fitness Assessment for Cancer Patients Using Multi-Model Decision Fusion Based on Multi-Source Data

Abstract

Objective and quantitative physical fitness assessment is the basis of pre-rehabilitation treatment for cancer patients, which can speed up the treatment process and reduce the adverse effects after treatment. In this study, in order to make up for the shortcomings of the traditional assessment by oncologists according to the scale, a method using multi-model decision fusion based on multi-source data is proposed. After fully considering the physical weakness of the cancer patients, a novel experimental paradigm is designed. The multi-source physiological data of 201 patients suffer from cancers have been collected and a series of features which can be used to assess the physical fitness are extracted. An improved data oversampling method based on the Mahalanobis distance and boundary constraints (MDBC) is proposed to overcome the problem of class imbalance. Moreover, a multi-model decision fusion (MMDF) method is proposed for classification, which combines multiple individual machine learning classifiers with a meta learner, to improve the accuracy of the physical fitness assessment. The recognition accuracy of our proposed method reaches 95.1%. The overall experimental results demonstrate that our proposed method is effective in physical fitness assessment of cancer patients.