Effective Parkinson Disease Detection and Prediction Using Voting Classifier in Machine Learning (original) (raw)

Abstract

The second most prevalent neurological condition, Parkinson’s disease causes considerable impairment, has no known treatment, and lowers a patient’s quality of life. Early detection may aid in preventing or reducing the symptoms. Because we are not progressing at a faster rate. There are numerous data and detection algorithms available for Parkinson’s disease. These are all distinct ethnic traditions. There are numerous databases for Parkinson’s disease currently available, including numerous internet sources and numerous hospital records. Parkinson disease forecasts are quite low because each is a distinct entity. The entire spectrum of medicinal options is not being fully exploited. There are numerous methods for using machine learning and artificial intelligence. Decision Tree Classifier, KNN Neighbors, Random Forest, and Logistic Regression are available for enhance efficacy and prediction. The dataset for Parkinson’s disease is used in the proposed work, and an ensemble model is created by combining all the best predictions made using different classification techniques. The suggested ensemble model with voting classifier beats all current classifiers in terms of high accuracy, precision, and recall for Parkinson disease diagnosis, detection, and prediction.

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References

  1. Laganas, C., et al.: Parkinson’s disease detection based on running speech data from phone calls. IEEE Trans. Biomed. Eng. 69(5), 1573–1584 (2022). https://doi.org/10.1109/TBME.2021.3116935
    Article PubMed Google Scholar
  2. Wang, W., Lee, J., Harrou, F., Sun, Y.: Early detection of Parkinson’s disease using deep learning and machine learning. IEEE Access 8, 147635–147646 (2020)
    Article Google Scholar
  3. Ali, L., Zhu, C., Zhang, Z., Liu, Y.: Automated detection of parkinson’s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network. IEEE J. Transl. Eng. Health Med. 7, 1–10 (2019)
    Article Google Scholar
  4. Grover, S., Bhartia, S., Akshama, Yadav, A., Seeja, K.R.: Predicting severity of Parkinson’s disease using deep learning. Procedia Comput. Sci. 132, 1788–1794 (2018)
    Google Scholar
  5. Guo, P.-F., Bhattacharya, P., Kharma, N.: Advances in detecting Parkinson’s disease. In: Zhang, D., Sonka, M. (eds.) Medical Biometrics, pp. 306–314. Springer Berlin Heidelberg, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13923-9_33
    Chapter Google Scholar
  6. Sivakumar, M., Hepzibah Christinal, A., Jebasingh, S.: Parkinson’s disease diagnosis using a combined deep learning approach. In: International Conference on Signal Processing and Communication, ICSPC (2021)
    Google Scholar
  7. Zhou, Y., Tinaz, S., Tagare, H.D.: Robust bayesian analysis of early-stage parkinson’s disease progression using DaTscan images. IEEE Trans. Med. Imaging 40(2), 549–561 (2021)
    Article ADS PubMed PubMed Central Google Scholar
  8. Haq, A.U., Ping Li, J., Memon, M.H.: Feature selection based on l1-norm support vector machine and effective recognition system for parkinson’s disease using voice recordings. In: IEEE Access (2019)
    Google Scholar
  9. Farzanehfar, P., et al.: Objective measurement in routine care of people with Parkinson’s disease improves outcomes. npj Parkinson’s Disease 4(1), 4 (2018)
    Article Google Scholar
  10. Iakovakis, D., et al.: Motor impairment estimates via touchscreen typing dynamics toward Parkinson’s disease detection from data harvested in-the-wild. Front. ICT 5(28), 1–13 (2018). https://doi.org/10.3389/fict.2018.00028
    Article Google Scholar
  11. Papadopoulos, A., Kyritsis, K., Klingelhoefer, L., Bostanjopoulou, S., Chaudhuri, K.R., Delopoulos, A.: Detecting Parkinsonian tremor from IMU data collected in-the-wild using deep multiple-instance learning. IEEE J. Biomed. Health Informat. 24(9), 2559–2569 (2019)
    Article Google Scholar
  12. Adrissi, J., Fleisher, J.: Moving the dial toward equity in Parkinson’s disease clinical research: a review of current literature and future directions in diversifying PD clinical trial participation. Curr. Neurol. Neurosci. Rep. 22(8), 475–483 (2022)
    Article PubMed PubMed Central Google Scholar
  13. Wang, L., et al.: Effects of dance therapy on non-motor symptoms in patients with Parkinson’s disease: a systematic review and meta-analysis. Aging Clin. Exp. Res. 34(6), 1201–1208 (2022). https://doi.org/10.1007/s40520-021-02030-7
    Article PubMed Google Scholar
  14. Martinez-Eguiluz, M., et al.: Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput. Appl. 35(8), 5603–5617 (2023)
    Article Google Scholar
  15. Mekha, P., Teeyasuksaet, N.: Hybrid machine learning classifier and ensemble techniques to detect Parkinson’s disease patients. SN Comput. Sci. 2, 189 (2021)
    Article Google Scholar

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Authors and Affiliations

  1. Department of Computational Intelligence, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation, Paiyanur, India
    T. R. Saravanan, Sasi Rekha, A. Jackulin Mahariba & N. Kanimozhi
  2. Department of Electrical and Electronics Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation, Paiyanur, India
    K. S. Kavitha Kumari
  3. Department of Information Technology, Mettu University, Mettu, Ethiopia
    Sridhar Udhayakumar
  4. SRM Institute of Science and Technology, Kattankulathur, India
    T. R. Saravanan, Sasi Rekha, A. Jackulin Mahariba & N. Kanimozhi

Authors

  1. T. R. Saravanan
  2. Sasi Rekha
  3. A. Jackulin Mahariba
  4. K. S. Kavitha Kumari
  5. N. Kanimozhi
  6. Sridhar Udhayakumar

Corresponding author

Correspondence toT. R. Saravanan .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Annie Uthra R.
  2. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Kottilingam Kottursamy
  3. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Gunasekaran Raja
  4. Manchester Metropolitan University, Manchester, UK
    Ali Kashif Bashir
  5. Department of Computer Engineering, Süleyman Demirel University, Isparta, Türkiye
    Utku Kose
  6. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Revathi Appavoo
  7. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Vimaladevi Madhivanan

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Saravanan, T.R., Rekha, S., Mahariba, A.J., Kumari, K.S.K., Kanimozhi, N., Udhayakumar, S. (2024). Effective Parkinson Disease Detection and Prediction Using Voting Classifier in Machine Learning. In: R., A.U., et al. Deep Sciences for Computing and Communications. IconDeepCom 2023. Communications in Computer and Information Science, vol 2176. Springer, Cham. https://doi.org/10.1007/978-3-031-68905-5\_21

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