IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v11y2024i5d10.1007_s40745-023-00482-4.html
   My bibliography  Save this article

Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network

Author

Listed:
  • Tarakashar Das

    (Premier University, Chattogram)

  • Sabrina Mobassirin

    (Premier University, Chattogram)

  • Syed Md. Minhaz Hossain

    (Premier University, Chattogram
    Chittagong University of Engineering and Technology)

  • Aka Das

    (Premier University, Chattogram)

  • Anik Sen

    (Premier University, Chattogram
    Chittagong University of Engineering and Technology)

  • Khaleque Md. Aashiq Kamal

    (Premier University, Chattogram)

  • Kaushik Deb

    (Chittagong University of Engineering and Technology)

Abstract

Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of artificial intelligence algorithms, there is an opportunity to develop a cost-effective system for diagnosing PD at an earlier stage. No permanent remedy has been established yet; however, an earlier diagnosis helps lead a better life. Probably, the three most responsible categories of symptoms for Parkinson’s Disease are tremors, rigidity, and body bradykinesia. Therefore, we investigate the 53 unique features of the Parkinson’s Progression Markers Initiative dataset to determine the significant symptoms, including three major categories. As feature selection is integral to developing a generalized model, we investigate including and excluding feature selection. Four feature selection methods are incorporated—low variance filter, Wilcoxon rank-sum test, principle component analysis, and Chi-square test. Furthermore, we utilize machine learning, ensemble learning, and artificial neural networks (ANN) for classification. Experimental evidence shows that not all symptoms are equally important, but no symptom can be completely eliminated. However, our proposed ANN model attains the best mean accuracy of 99.51%, 98.17% mean specificity, 0.9830 mean Kappa Score, 0.99 mean AUC, and 99.70% mean F1-score with all the features. The efficiency of our suggested technique on diverse data modalities is demonstrated by comparison with recent publications. Finally, we established a trade-off between classification time and accuracy.

Suggested Citation

  • Tarakashar Das & Sabrina Mobassirin & Syed Md. Minhaz Hossain & Aka Das & Anik Sen & Khaleque Md. Aashiq Kamal & Kaushik Deb, 2024. "Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network," Annals of Data Science, Springer, vol. 11(5), pages 1821-1864, October.
  • Handle: RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-023-00482-4
    DOI: 10.1007/s40745-023-00482-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-023-00482-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40745-023-00482-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Leila Sh. Krupenikova (Крупеникова Л.Ш.) & Vladimir I. Kurbatov (Курбатов В.И.), 2022. "Big Data: New Organizational Opportunities And Social Risks [Big Data: Новые Организационные Возможности И Социальные Риски]," State and Municipal Management Scholar Notes, Russian Presidential Academy of National Economy and Public Administration, vol. 2, pages 247-251.
    3. Vrushabh Gada & Madhura Shegaonkar & Madhura Inamdar & Sharath Dinesh & Darshan Sapariya & Vedant Konde & Mahesh Warang & Ninad Mehendale, 2022. "Data Analysis of COVID-19 Hospital Records Using Contextual Patient Classification System," Annals of Data Science, Springer, vol. 9(5), pages 945-965, October.
    4. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Li & Guangle Du, 2024. "Reaction Function for Financial Market Reacting to Events or Information," Annals of Data Science, Springer, vol. 11(4), pages 1265-1290, August.
    2. Reetika Sarkar & Sithija Manage & Xiaoli Gao, 2024. "Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations," Annals of Data Science, Springer, vol. 11(4), pages 1139-1164, August.
    3. Mahabuba Akhter & Syed Md. Minhaz Hossain & Rizma Sijana Nigar & Srabanti Paul & Khaleque Md. Aashiq Kamal & Anik Sen & Iqbal H. Sarker, 2024. "COVID-19 Fake News Detection using Deep Learning Model," Annals of Data Science, Springer, vol. 11(6), pages 2167-2198, December.
    4. Sakib A. Mondal & Prashanth Rv & Sagar Rao & Arun Menon, 2024. "LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems," Annals of Data Science, Springer, vol. 11(4), pages 1165-1183, August.
    5. F. Gauthier & D. Germain & B. Hétu, 2017. "Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie, Québec, Canada," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 201-232, October.
    6. Douglas Cumming & Lars Hornuf & Moein Karami & Denis Schweizer, 2023. "Disentangling Crowdfunding from Fraudfunding," Journal of Business Ethics, Springer, vol. 182(4), pages 1103-1128, February.
    7. Eunae Yoo & Elliot Rabinovich & Bin Gu, 2020. "The Growth of Follower Networks on Social Media Platforms for Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2696-2715, December.
    8. Lo Turco, Alessia & Maggioni, Daniela, 2018. "Effects of Islamic religiosity on bilateral trust in trade: The case of Turkish exports," Journal of Comparative Economics, Elsevier, vol. 46(4), pages 947-965.
    9. Blackman, Allen & Guerrero, Santiago, 2012. "What drives voluntary eco-certification in Mexico?," Journal of Comparative Economics, Elsevier, vol. 40(2), pages 256-268.
    10. Alessandra Iannamorelli & Stefano Nobili & Antonio Scalia & Luana Zaccaria, 2024. "Asymmetric Information and Corporate Lending: Evidence from SME Bond Markets," Review of Finance, European Finance Association, vol. 28(1), pages 163-201.
    11. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    12. Mehrez Ben Slama & Dhafer Saidane & Hassouna Fedhila, 2012. "How to identify targets in the M&A banking operations? Case of cross-border strategies in Europe by line of activity," Review of Quantitative Finance and Accounting, Springer, vol. 38(2), pages 209-240, February.
    13. Lorenzo Cassi & Anne Plunket, 2014. "Proximity, network formation and inventive performance: in search of the proximity paradox," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 395-422, September.
    14. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    15. Xinfu Xing & Chenglong Wu & Jinhui Li & Xueyou Li & Limin Zhang & Rongjie He, 2021. "Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 97-117, March.
    16. Hwang, Seokyoun & Sarath, Bharat & Han, Seung-youb, 2022. "Auditor independence: The effect of auditors’ quality control efforts and corporate governance," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 47(C).
    17. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    18. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    19. Andrews, RJ & Fazio, Catherine & Guzman, Jorge & Liu, Yupeng & Stern, Scott, 2022. "The Startup Cartography Project: Measuring and mapping entrepreneurial ecosystems," Research Policy, Elsevier, vol. 51(2).
    20. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-023-00482-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.