Parkinson’s Disease Diagnosis by Adaptive Boosting and Classification Tree using Voice Features

Authors

  • R R Janghel NIT Raipur
  • Chandra Prakash Rathore Dr. C. V. Raman University, Kota, Bilaspur, Chhattisgarh, India
  • Kshitiz Varma Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
  • Swati Rathore Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India

DOI:

https://doi.org/10.30732/ijbbb.20170201002

Keywords:

Adaptive Boosting, Classification Tree, Principal Component Analysis, Correlation, Bradykinesia

Abstract

Parkinson’s disease is a widespread disease among elder population worldwide effecting approximately 6.3 million people across all genders, races and cultures. It is caused by dopamine loss, a chemical mediator that is responsible for body’s ability to control the movements. The disease reduces quality of life because of motor and non-motor complications. In this article Adaptive Boosting and Classification Tree based soft computing models are implemented to diagnose Parkinson’s disease using voice features. The soft computing models performances are evaluated on performance measures viz. true positive, false positive, false negative, true negative, accuracy, sensitivity, specificity, RMSE on training and datasets. Finally a comparison is performed to identify the most efficient model and dataset combination. Adaptive Boosting  model outperformed others on reduced feature vector dataset obtained by selecting prominent 15 principal components using principal component analysis, where, it demonstrated 100% accuracy, 100% sensitivity, 100% specificity, 0.0 RMSE on training dataset and 67.00% accuracy, 67.35% sensitivity, 66.67% specificity, 0.5745 RMSE on testing dataset.

References

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Published

2017-06-22

How to Cite

Janghel, R. R., Rathore, C. P., Varma, K., & Rathore, S. (2017). Parkinson’s Disease Diagnosis by Adaptive Boosting and Classification Tree using Voice Features. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, 2(1), 07–12. https://doi.org/10.30732/ijbbb.20170201002

Issue

Vol. 2 No. 1 (2017): Peer Review

Section

Articles

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