Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data
ABSTARCT :
Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the l2,p norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.
EXISTING SYSTEM :
? Nevertheless, most existing research only focus on binary classification to differentiate PD and normal control (NC). A third category called scan without evidence of dopaminergic deficit (SWEDD) lacks sufficient attention.
? With the development of machine learning and data-driven analysis, a great number of recent studies have been proposed to predict and assess the stage of pathology using the brain images.
? An accurate recognition of SWEDD contributes to offer appropriate therapeutic options to patients .
? Accordingly, we simultaneously classify three different clinical statuses for practical clinical application instead of binary classification of NC vs. SWEDD or PD vs. SWEDD.
DISADVANTAGE :
? An effective iterative optimization algorithm is proposed to solve this problem.
? Nevertheless, for the multimodal data, the small dataset size and large feature dimension typically cause overfitting problem and renders difficulty in model generalization.
? Due to the existence of data loss problem on longitudinal time points, we use the 5-fold cross-validation approach to verify the proposed approach on the 12-month and 24-month data.
? By constructing the united embedding and sparse regression framework, our approach can find the most disease-related biomarkers, which is helpful for PD monitoring.
PROPOSED SYSTEM :
• Our proposed method is evaluated on the public available Parkinson’s progression markers initiative (PPMI) datasets.
• Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods.
• In our proposed method, we first set the feature selection tuning parameters at a certain range and then select the most suitable values in the process of the experiments.
• We compare our proposed method with other widely used methods such as elastic net, least absolute shrinkage and selection operator (Lasso) , Multi-modal multi-task (M3T).
ADVANTAGE :
? We use longitudinal data to boost the performance of regression and classification effectively. The proposed approach is shown to surpass other state-of-the-art methods.
? Though deep learning has been extensively used in the medical image fields, it is difficult to obtain excellent generalization performance on small number of subjects.
? We conduct abundant experiments on the PPMI dataset to verify the effectiveness of the proposed approach.
? The results show that our algorithm effectively boosts the performance of classification and clinical score regression and surpasses other state-of-the-art approaches by taking full advantage of the longitudinal data.
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