Title: Early detection of Parkinson's disease through multimodal features using machine learning approaches
Authors: Gunjan Pahuja; T.N. Nagabhushan; Bhanu Prasad; Ravi Pushkarna
Addresses: Department of Computer Science & Engineering, JSSATE Noida, Dr. A.P.J Abdul Kalam Technical University, Uttar Pradesh 201301, India ' Department of Information Science & Engineering, Sri Jayachamarajendra College of Engineering, Mysuru 570006, India ' Department of Computer and Information Sciences, Florida A&M University, Tallahassee, Florida 32307, USA ' Department of Radiology, Max Hospital, Noida, Uttar Pradesh 201301, India
Abstract: This research establishes a relation between objective biomarkers of Parkinson's disease (PD) based on T1-weighted MRI scans and other clinical biomarkers. It shall aid doctors in identifying the onset and progression of PD among the patients. Voxel-based morphometry has been used for feature extraction from MRI scans. These extracted features are combined with biochemical biomarkers for dataset enrichment. A genetic algorithm is applied to this dataset to remove the redundancies and to obtain an optimal set of features. Subsequently, we used Self-adaptive resource allocation network (SRAN), extreme learning machine (ELM) and support vector machines (SVM) to classify different subjects. It is observed that SRAN classifier gave the best performance when compared with ELM and SVM. Finally, it is found that a variation of grey matter in Thalamus is responsible for PD. The obtained results corroborate the earlier findings from the literature.
Keywords: Parkinson's disease; magnetic resonance imaging; MRI; proteomic biomarkers; genetic algorithms; GA; classification; self-adaptive resource allocation network; SRAN; extreme learning machine; ELM; support vector machines; SVM.
International Journal of Signal and Imaging Systems Engineering, 2018 Vol.11 No.1, pp.31 - 43
Received: 14 Aug 2017
Accepted: 08 Oct 2017
Published online: 13 Mar 2018 *