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Detection of Parkinson’s Disease Using Voice and Spiral Drawings on Machine Learning Approaches |
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| รหัสดีโอไอ | |
| Creator | P. Bhuvaneswari |
| Title | Detection of Parkinson’s Disease Using Voice and Spiral Drawings on Machine Learning Approaches |
| Contributor | V. Madhurima, Mohammed Mahaboob Basha, Srinivasulu Gundala, Mohan Dholvan |
| Publisher | Faculty of Engineering Mahasasakham University |
| Publication Year | 2568 |
| Journal Title | Engineering Access |
| Journal Vol. | 11 |
| Journal No. | 2 |
| Page no. | 163-169 |
| Keyword | Dataset, Efficiency, Random Forest algorithm, XGBoost, Parkinson's disease |
| URL Website | https://ph02.tci-thaijo.org/index.php/mijet/index |
| Website title | THAIJO Engineering Access |
| ISSN | 2730-4175 |
| Abstract | Parkinson's disease (PD) is a neurologically development disease that beginnings with a mild quivers in limbs and affect firmness of the body. Over 6 million individuals around the globe are affected due to this disease. It’s very hard to identify PD in the early stages as there is no particular analysis for this condition and limited expert specialists at present. This recommended procedure for diagnoses of PD by using spiral drawings and speech taken from the patient using eXtreme-Gradient Boosting (XGBoost) algorithms and Random-Forest algorithm pilot to higher efficiency and higher than 74%. The speech samples of the person are studied to identify this sickness. The data from the patients affected by this disease, and non effected people are considered while training this algorithm. From the dataset 40% of voice data is utilized for testing the model, and 60% of spiral data is utilized for training the model. The speech data is represented in 24 columns, represents the status of the person either diseased or healthy. These factors are considered in identifying the disease where 1’s in status column indicate affected person and 0’s indicate healthy person. Mean, standard deviation, jitter, noise to harmonics and harmonics to noise ratio are some of the parameters taken into consideration for predicting the disease for the speech data. |
