Abstract
The Parkinson’s disease is a neurodegenerative disorder that has affected millions of people and found mostly in aged people. It occurs due to loss of dopaminergic neurons in substantia nigra part which is found in thalamic region of human brain. Diagnosis of Parkinson’s disease is very much costly. Most of the research works that have been performed to detect Parkinson’s disease are based on speech utterances, kinematic features, pen-based features, etc. In this paper, we aim to simplify the process for early detection of Parkinson’s disease by relying only on hand-drawn figures taken from the disease-affected patients. Histogram of oriented gradients features has been extracted from different types of images, which act as an input to various machine learning classifiers such as k-nearest neighbour, random forest, support vector machine, Naïve Bayes, multi-layer perceptron, and the performance of different classifiers is shown. Experimental result analysis shows that with the available training datasets, Dataset 1 and Dataset 2 have achieved 74.7% and 96.8% of accuracy, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mohamed GS (2016) Parkinson’s disease diagnosis: detecting the effect of attributes selection and discretization of Parkinson’s disease dataset on the performance of classifier algorithms. Open Access Libr J 3(11):1–11
Hariharan M, Polat K, Sindhu R (2014) A new hybrid intelligent system for accurate detection of Parkinson’s disease. Comput Methods Programs Biomed 113(3):904–913
Aich S, Younga K, Hui KL, Al-Absi AA, Sain M (2018) A nonlinear decision tree based classification approach to predict the Parkinson’s disease using different feature sets of voice data. In: 20th international conference on advanced communication technology (ICACT). IEEE, pp 638–642
Peker M, Sen B, Delen D (2015) Computer-aided diagnosis of Parkinson’s disease using complex-valued neural networks and mRMR feature selection algorithm. J Healthcare Eng 6(3):281–302
Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2016) Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif Intell Med 67:39–46
Loconsole C, Trotta GF, Brunetti A, Trotta J, Schiavone A, Tatò SI, Losavio G, Bevilacqua V (2017) Computer vision and EMG-based handwriting analysis for classification in Parkinson’s disease. In: International conference on intelligent computing. Springer, pp 493–503
Pereira CR, Weber SA, Hook C, Rosa GH, Papa JP (2017) Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In: 29th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 340–346
Folador JP, Rosebrock A, Pereira AA, Vieira MF, de Oliveira Andrade A (2019) Classification of handwritten drawings of people with Parkinson’s disease by using histograms of oriented gradients and the random forest classifier. In: Latin American conference on biomedical engineering. Springer, Cham, pp 334–343
Zham P, Kumar DK, Dabnichki P, Poosapadi Arjunan S, Raghav S (2017) Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Frontiers Neurol 8:435
Bernardo LS, Quezada A, Munoz R, Maia FM, Pereira CR, Wu W, de Albuquerque VHC (2019) Handwritten pattern recognition for early Parkinson’s disease diagnosis. Pattern Recogn Lett 125:78–84
Athitsos V, Sclaroff S (2005) Boosting nearest neighbor classifiers for multiclass recognition. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’05)-Workshops. IEEE, pp 45–45
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Bustomi MA, Faricha A, Ramdhan A, Faridawati (2018) Integrated image processing analysis and naive Bayes classifier method for lungs X-ray image classification. ARPN J Eng Appl Sci 13(2):718–724
Kanafiah SNAM, Ali H, Firdaus AA, Azalan MZ, Jusman Y, Khairi AA, Ahmad MR, Sara T, Amran T, Mansor I, Shukor SAA (2019) Metal shape classification of buried object using multilayer perceptron neural network in GPR data. IOP Conf Ser Mater Sci Eng 705(1):012028. IOP Publishing (2019)
Acknowledgements
This work is a part of the project that has been sponsored by Assam Science and Technology University (ASTU), Guwahati, under Collaborative Research Scheme of TEQIP-III via grant no. ASTU/TEQIP-III/Collaborative Research/2019/2479.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, A., Das, H.S., Neog, A., Bharat Reddy, B., Swargiary, M. (2021). Performance Analysis of Different Machine Learning Classifiers in Detection of Parkinson’s Disease from Hand-Drawn Images Using Histogram of Oriented Gradients. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-33-4604-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4603-1
Online ISBN: 978-981-33-4604-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)