A new EFMM-OneR hybrid model for diagnosing parkinson's disease
Parkinson's disease is a dangerous disease that attacks the nervous system and affects it negatively over time. Early diagnosis of this disease is necessary for identifying the most appropriate treatment for preventing the disease from worsening. It can be diagnosed by examining the symptoms of...
| Principais autores: | , |
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| Formato: | Conference or Workshop Item |
| Idioma: | English |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf |
| _version_ | 1825812544100499456 |
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| author | Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah |
| author_facet | Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah |
| author_sort | Al Sayaydeh, Osama Nayel |
| collection | UMP |
| description | Parkinson's disease is a dangerous disease that attacks the nervous system and affects it negatively over time. Early diagnosis of this disease is necessary for identifying the most appropriate treatment for preventing the disease from worsening. It can be diagnosed by examining the symptoms of the patient. Recently, researchers have used voice disorders to diagnose Parkinson's disease by extracting attributes from audio recordings of affected people and using classification techniques to provide accurate diagnoses. In this paper, an enhanced fuzzy min-max neural network based on the OneR attribute evaluator (EFMM-OneR) is proposed as a hybrid model for diagnosing Parkinson's disease. The hybrid model consists of two stages: In the first stage, feature selection is used to identify and remove irrelevant, redundant, or noisy features from the provided dataset. In the second stage, the enhanced fuzzy min-max (EFMM) neural network is used for the classification process. The results demonstrated the ability of the EFMM-OneR model to improve the classification accuracy as compared to other classifiers from the literature. |
| first_indexed | 2024-03-06T12:30:45Z |
| format | Conference or Workshop Item |
| id | UMPir24055 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:30:45Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | UMPir240552019-06-10T07:03:08Z http://umpir.ump.edu.my/id/eprint/24055/ A new EFMM-OneR hybrid model for diagnosing parkinson's disease Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah QA75 Electronic computers. Computer science Parkinson's disease is a dangerous disease that attacks the nervous system and affects it negatively over time. Early diagnosis of this disease is necessary for identifying the most appropriate treatment for preventing the disease from worsening. It can be diagnosed by examining the symptoms of the patient. Recently, researchers have used voice disorders to diagnose Parkinson's disease by extracting attributes from audio recordings of affected people and using classification techniques to provide accurate diagnoses. In this paper, an enhanced fuzzy min-max neural network based on the OneR attribute evaluator (EFMM-OneR) is proposed as a hybrid model for diagnosing Parkinson's disease. The hybrid model consists of two stages: In the first stage, feature selection is used to identify and remove irrelevant, redundant, or noisy features from the provided dataset. In the second stage, the enhanced fuzzy min-max (EFMM) neural network is used for the classification process. The results demonstrated the ability of the EFMM-OneR model to improve the classification accuracy as compared to other classifiers from the literature. 2019-01-25 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf Al Sayaydeh, Osama Nayel and Mohammed, Mohammed Falah (2019) A new EFMM-OneR hybrid model for diagnosing parkinson's disease. In: 2nd International Conference on Advanced Science and Engineering 2019 (ICOASE2019) , 2-4 April 2019 , Duhok, Kurdistan Region-Iraq. pp. 1-6.. (Submitted) (Submitted) |
| spellingShingle | QA75 Electronic computers. Computer science Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title | A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title_full | A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title_fullStr | A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title_full_unstemmed | A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title_short | A new EFMM-OneR hybrid model for diagnosing parkinson's disease |
| title_sort | new efmm oner hybrid model for diagnosing parkinson s disease |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf |
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