Early diagnosis of Parkinson?s disease: A combined method using deep learning and neuro-fuzzy techniques

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total-UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.

Automated summary

In this research, a combined approach using Deep Belief Network (DBN) and Neuro-Fuzzy methods is proposed for Parkinson's disease diagnosis. Large datasets are handled using the Expectation-Maximization (EM) clustering approach. Principle Component Analysis (PCA) is used for noise removal. The approach is assessed on a real-world PD dataset, showing improved UPDRS prediction accuracy and lower time complexity in handling large datasets compared to previous methods.