Evaluating the performance of three classification methods in diagnosis of parkinson�s disease
Classification (of information); Computer aided diagnosis; Decision trees; Diagnosis; Neural networks; Sodium; Soft computing; Accuracy rate; Classification methods; Classification results; Medical history; Neural network classification; Neurological examination; System conditions; Voice disorders;...
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Springer Verlag
2023
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my.uniten.dspace-242222023-05-29T14:57:08Z Evaluating the performance of three classification methods in diagnosis of parkinson�s disease Mostafa S.A. Mustapha A. Khaleefah S.H. Ahmad M.S. Mohammed M.A. 37036085800 57200530694 57188929678 56036880900 57192089894 Classification (of information); Computer aided diagnosis; Decision trees; Diagnosis; Neural networks; Sodium; Soft computing; Accuracy rate; Classification methods; Classification results; Medical history; Neural network classification; Neurological examination; System conditions; Voice disorders; Data mining Accurate diagnosis of the Parkinson�s disease is a challenging task that involves many physical, psychological and neurological examinations. The examinations include investigating a number of signs and symptoms, reviewing the medical history and checking the nervous system conditions of a patient. Recently, researchers use voice disorders to diagnose Parkinson�s disease patients. They extract features of a recorded human voice and apply classification methods to diagnosis this disease. In this paper, we apply a Decision Tree, Na�ve Bayes and Neural Network classification methods for the diagnosis of Parkinson�s disease. The aim of this paper is to resolve the problem by evaluating the performance of the three methods. The objectives of the paper are to (i) implement three classification methods independently on a Parkinson�s dataset, and (ii) determine the best method among the three. The classification results show that the Decision Tree produces the highest accuracy rate of 91.63%, followed by the Neural Network, 91.01% and the Na�ve Bayes produces the lowest accuracy rate of 89.46%. The results recommend using the Decision Tree or the Neural Network over the Na�ve Bayes for datasets with similar properties. � 2018, Springer International Publishing AG. Final 2023-05-29T06:57:08Z 2023-05-29T06:57:08Z 2018 Conference Paper 10.1007/978-3-319-72550-5_5 2-s2.0-85041548143 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041548143&doi=10.1007%2f978-3-319-72550-5_5&partnerID=40&md5=b2987fe41932f99c1bb9e208ed1f1255 https://irepository.uniten.edu.my/handle/123456789/24222 700 43 52 Springer Verlag Scopus |
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| description |
Classification (of information); Computer aided diagnosis; Decision trees; Diagnosis; Neural networks; Sodium; Soft computing; Accuracy rate; Classification methods; Classification results; Medical history; Neural network classification; Neurological examination; System conditions; Voice disorders; Data mining |
| author2 |
37036085800 |
| author_facet |
37036085800 Mostafa S.A. Mustapha A. Khaleefah S.H. Ahmad M.S. Mohammed M.A. |
| format |
Conference Paper |
| author |
Mostafa S.A. Mustapha A. Khaleefah S.H. Ahmad M.S. Mohammed M.A. |
| spellingShingle |
Mostafa S.A. Mustapha A. Khaleefah S.H. Ahmad M.S. Mohammed M.A. Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| author_sort |
Mostafa S.A. |
| title |
Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| title_short |
Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| title_full |
Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| title_fullStr |
Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| title_full_unstemmed |
Evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| title_sort |
evaluating the performance of three classification methods in diagnosis of parkinson�s disease |
| publisher |
Springer Verlag |
| publishDate |
2023 |
| _version_ |
1806423497852846080 |
| score |
13.214268 |