Multi-modal biological feature selection for Parkinson's disease staging based on binary PSO with broad learning

Parkinson's disease is a nerve system disease that seriously affects the patients’ quality of life. Parkinson's disease staging can be realized based on intelligent methods according to the extent of disease, and the search for appropriate biomarkers is beneficial to patients’ management and treatment and relieves medical pressure greatly. Parkinson's disease is often accompanied by a variety of motor and non-motor symptoms, so for one patient, multiple biological signals can be collected from which multiple features can be extracted using various methods. These biological signals and features have multiple modalities. In this context, this article obtains multi-modal biological signals by some measuring devices, and proposes multi-modal biological feature selection based on multi-modal binary particle swarm optimization (MM-BPSO) with multi-modal broad learning (MM-BL) to realize Parkinson's disease staging. The MM-BPSO combines classification metrics with importance and correlation of features, holds the independence and achieves complementarity of different modal features, and provides a reference for clinical biomarkers. The MM-BL consists of multiple sub-broad learning structures and softmax layers, ensuring that the predictions of each modality for all classes are fully considered, which reflects the severity of multiple symptoms. Experiments are carried out on the collected multi-modal Parkinson's motor symptom datasets, the results indicate that multi-modal classification of the selected biological feature subsets has a better performance than other methods.