Abstract
Parkinson’s disease, which affects millions of people worldwide, is a term used to describe a neurological and neurodegenerative movement disorder. Common symptoms include a loss of automatic motions and muscle rigidity, which ultimately result in problems with balance, coordination, and walking. The patient’s physical, emotional, and mental health gradually worsens as a result of these symptoms. Before the patient’s health worsens, therapeutic care can be given to lower the disease’s prognosis. It is possible to predict whether or not a person has Parkinson’s disease using machine learning classification algorithms. This can lengthen the lives of older individuals and improve their quality of life when they have Parkinson’s. This study suggests a potential technique to identify Parkinson’s disease symptoms in their early stages. Based on the speech input parameters, algorithms like Gradient Boosting, XGBoost, Random Forest, and Extra Trees Classification are used to estimate whether the individual is normal or affected by Parkinson’s disease. According to this study, the ensemble method Gradient Boosting classification algorithm outperformed other classification algorithms in terms of test accuracy rate (95%). The effectiveness of the approaches was evaluated using a reliable dataset from the UCI Machine Learning library.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harvey, J., et al.: Machine learning-based prediction of cognitive outcomes in Parkinson’s disease. MedRxiv (2022)
Aarsland, D., Creese, B., Politis, M., Chaudhuri, K., Weintraub, D., Ballard, C.: Cognitive decline in Parkinson disease. Nat. Rev. Neurol. 13(4), 217–231 (2017)
Liu, G., et al.: Prediction of cognition in Parkinson’s disease with a clinical-genetic score: a longitudinal analysis of nine cohorts. Lancet Neurol. 16(8), 620–629 (2017)
Phongpreecha, T., et al.: Multivariate prediction of dementia in Parkinson’s disease. NPJ Parkinson’s Dis. 6(1), 1–10 (2020)
James, C., Ranson, J.M., Everson, R., Llewellyn, D.J.: Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients. JAMA Netw. Open 4(12), e2136553 (2021)
Rana, A., Dumka, A., Singh, R., Panda, M.K., Priyadarshi, N., Twala, B.: Imperative role of machine learning algorithm for detection of Parkinson’s disease: review, challenges, and recommendations. Diagnostics 12(8), 2022 (2003)
Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1) (2019). Article number: 20. https://doi.org/10.1186/s42400-019-0038-7
Asmae, O., Abdelhadi, R., Bouchaib, C., Sara, S., Tajeddine, K.: Parkinson’s disease identification using KNN and ANN Algorithms based on Voice Disorder. In: 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (2020)
Agarwal, A., Chandrayan, S., Sahu, S.S.: Prediction of Parkinson’s disease using speech signal with Extreme Learning Machine. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (2016)
Bansal, M., Upali, S.J.R., Sharma, S.: Early Parkinson disease detection using audio signal processing. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Piuri, V. (eds.) Emerging Technologies in Data Mining and Information Security. LNNS, vol. 491, pp. 243–250. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-4193-1_23
Mittal, V., Sharma, R.K.: Machine learning approach for classification of Parkinson disease using acoustic features. J. Reliable Intell. Environ. 7, 233–239 (2021). https://doi.org/10.1007/s40860-021-00141-6
Zhao, H., Wang, R., Lei, Y., Liao, W.-H., Cao, H., Cao, J.: Severity level diagnosis of Parkinson’s disease by ensemble K-nearest neighbor under imbalanced data. Expert Syst. Appl. 189, 116113 (2022)
Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., Venkatraman, V.: Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Gener. Comput. Syst. 83, 366–373 (2018)
Rohit Surya, A.T., Yaswanthram, P., Nair, P.R., Rajendra Prasath, S.S., Akella, S.V.V.S.: Prediction of Parkinson’s disease using machine learning models—a classifier analysis. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. LNNS, vol. 218, pp. 453–460. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_35
Ahmed, I., Aljahdali, S., Khan, M.S., Kaddoura, S.: Classification of Parkinson disease based on patient’s voice signal using machine learning. Intell. Autom. Soft Comput. 32(2), 705–722 (2022)
Sarker, I.H., Kayes, A.S.M., Badsha, S., Alqahtani, H., Watters, P., Ng, A.: Cybersecurity data science: an overview from machine learning perspective. J. Big Data 7(1) (2020). Article number: 41. https://doi.org/10.1186/s40537-020-00318-5
Dutt, I., Borah, S., Maitra, I.K., Bhowmik, K., Maity, A., Das, S.: Real-time hybrid intrusion detection system using machine learning techniques. In: Bera, R., Sarkar, S.K., Chakraborty, S. (eds.) Advances in Communication, Devices and Networking. LNEE, vol. 462, pp. 885–894. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7901-6_95
Jeon, H., et al.: Automatic classification of tremor severity in Parkinson’s disease using a wearable device. Sensors 17(9), 2067 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sumalatha, U., Krishna Prakasha, K., Prabhu, S., Nayak, V.C. (2023). Analysis of Classification Algorithms for Predicting Parkinson’s Disease and Applications in the Field of Cybersecurity. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-2264-2_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2263-5
Online ISBN: 978-981-99-2264-2
eBook Packages: Computer ScienceComputer Science (R0)