Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels
Article
Barua, Prabal Datta, Aydemir, Emrah, Dogan, Sengul, Erten, Mehmet, Kaysi, Feyzi, Tuncer, Turker, Fujita, Hamido, Palmer, Elizabeth and Acharya, U Rajendra. 2023. "Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels." Neural Computing and Applications. 35 (8), pp. 6065-6077. https://doi.org/10.1007/s00521-022-07999-4
| Article Title | Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels |
|---|---|
| ERA Journal ID | 18089 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Aydemir, Emrah, Dogan, Sengul, Erten, Mehmet, Kaysi, Feyzi, Tuncer, Turker, Fujita, Hamido, Palmer, Elizabeth and Acharya, U Rajendra |
| Journal Title | Neural Computing and Applications |
| Journal Citation | 35 (8), pp. 6065-6077 |
| Number of Pages | 13 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United Kingdom |
| ISSN | 0941-0643 |
| 1433-3058 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-022-07999-4 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s00521-022-07999-4 |
| Abstract | Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model. |
| Keywords | Favipiravir pattern |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Business |
| University of Technology Sydney | |
| Sakarya University, Turkey | |
| Firat University, Turkey | |
| Public Health Lab, Turkey | |
| Istanbul University, Turkiye | |
| University of Granada, Spain | |
| Iwate Prefectural University, Japan | |
| HUTECH University of Technology, Vietnam | |
| Sydney Children's Hospital, Australia | |
| University of New South Wales | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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