Barua Prabal Datta, Aydemir Emrah, Dogan Sengul, Erten Mehmet, Kaysi Feyzi, Tuncer Turker, Fujita Hamido, Palmer Elizabeth, Acharya U Rajendra
School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia.
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia.
Neural Comput Appl. 2023;35(8):6065-6077. doi: 10.1007/s00521-022-07999-4. Epub 2022 Nov 13.
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.
特定语言障碍（SLI）是儿童中最常见的疾病之一，早期诊断有助于在经济上获得更好的及时治疗。临床医生通过标准临床评估准确检测SLI既困难又耗时。因此，已开发机器学习算法来辅助SLI的准确诊断。这项工作旨在研究基于法匹拉韦分子的特征提取函数的图形，并提出一种使用元音的准确SLI检测模型。我们提出了一种新颖的手工制作的机器学习框架。该架构包括法匹拉韦分子结构模式、统计特征提取器、小波包分解（WPD）、迭代邻域成分分析（INCA）和支持向量机（SVM）分类器。在手工制作的特征生成方法中采用了两种特征提取模型，即统计模型和纹理模型。一种新的基于自然启发的图形特征提取器，它使用法匹拉韦的化学描述（法匹拉韦在新冠疫情期间广为人知）用于特征提取。最后，所提出的法匹拉韦模式、统计特征提取器和小波包分解用于创建特征向量。此外，这项工作中使用了统计特征提取器。WPD生成多级特征，并使用NCA特征选择器选择最有意义的特征。最后，将这些选定的特征输入到SVM分类器进行自动分类。使用两种验证方法，即（i）留一法（LOSO）和（ii）十折交叉验证（CV）来获得稳健的分类结果。我们使用元音数据集开发的基于法匹拉韦模式的模型，分别使用十折交叉验证和留一法策略检测SLI儿童的准确率为99.87%和98.86%。这些结果证明了所提出的基于法匹拉韦模式的模型具有很高的元音分类能力。
