Joint Detection And Clinical Score Prediction In Parkinson’s Disease Via Multi-Modal Features

Parkinson’s disease(PD)is the world’s second most common progressive neurodegenerative disease.This disease is characterized by a combination of various non-motor symptoms(e.g.,depression,olfactory and sleep disturbance)and motor symptoms(e.g.,bradykinesia,tremor,rigidity),furthermore,although it is known that these symptoms are derived from the degeneration of a type of nerve cell called dopamine,there is still no definitive cure for PD because the cause for death of this cell is still unknown.therefore diagnosis and treatment of PD are usually complex.There are some machine learning techniques that automate PD diagnosis and predict clinical scores,and deliver promising results in assisting assessment of the stage of pathology and predicting PD progression.However,existing PD research mainly focuses on single-function model(i.e.,only classification or prediction)using one modality.In general,there are three key steps for classification and regression,i.e.,feature extraction,feature selection,and classification or regression.In the field of medical image analysis,it is challenging to select informative features to solve the problem of small sample size and high feature dimension.For this reason,we propose a new united novel feature selection framework via multi-modal neuroimaging data for PD diagnosis,which simultaneously performs classification and clinical sores prediction based on a novel loss function.Specifically,a unique objective function is designed to capture discriminative features used to train support vector regression model for predicting clinical score(e.g.,sleep scores and olfactory scores),and support vector classification model for class label identification.We evaluate our method using a dataset of 208 subjects,which includes 56 normal controls,123 PD and 29 scans without evidence of dopamine deficit via a 10-fold cross-validation strategy.Our experimental results demonstrate that multi-modal data effectively improves the performance in disease status identification and clinical scores prediction as compared to one single modality.Comparative analysis reveals that the proposed method outperforms state-of-art methods.