Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AETHX9L5G" target="_blank" >RIV/00216208:11320/22:ETHX9L5G - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/23:I26WQDID

Result on the web

<a href="https://doi.org/10.1007/s00521-022-07999-4" target="_blank" >https://doi.org/10.1007/s00521-022-07999-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-022-07999-4" target="_blank" >10.1007/s00521-022-07999-4</a>

Result language

angličtina

Original language name

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Computing and Applications [online]

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

35

Issue of the periodical within the volume

8

Country of publishing house

TR - TURKEY

Number of pages

13

Pages from-to

6065-6077

UT code for WoS article

000882759300003

EID of the result in the Scopus database

2-s2.0-85141870329