Int. J. Signal and Imaging Systems Engineering, Vol. 11, No. 1, 2018 31 Copyright © 2018 Inderscience Enterprises Ltd. Early detection of Parkinson’s disease through multimodal features using machine learning approaches Gunjan Pahuja* Department of Computer Science & Engineering, JSSATE Noida, Dr. A.P.J Abdul Kalam Technical University, Uttar Pradesh 201301, India Email: gunjanpahuja@jssaten.ac.in *Corresponding author T.N. Nagabhushan Department of Information Science & Engineering, Sri Jayachamarajendra College of Engineering, Mysuru 570006, India Email: tnn@sjce.ac.in Bhanu Prasad Department of Computer and Information Sciences, Florida A&M University, Tallahassee, Florida 32307, USA Email: bhanu.prasad@famu.edu Ravi Pushkarna Department of Radiology, Max Hospital, Noida, Uttar Pradesh 201301, India Email: buddharadiologist@yahoo.co.in 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. Reference to this paper should be made as follows: Pahuja, G., Nagabhushan, T.N., Prasad, B. and Pushkarna, R. (2018) ‘Early detection of Parkinson’s disease through multimodal features using machine learning approaches’, Int. J. Signal and Imaging Systems Engineering, Vol. 11, No. 1, pp.31–43. Biographical notes: Gunjan Pahuja received her MTech from Guru Jambheshwar University of Science and Technology in 2006. Currently, she is working towards the PhD in the Department of Computer Science and Engineering from Dr. A.P.J. Abdul Kalam University, Lucknow. Her research focuses on machine learning and medical image processing. She is a Member of ISTE and CSI.