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Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depress...

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Autores principales: Saba, Tanzila, Khan, Amjad Rehman, Abunadi, Ibrahim, Bahaj, Saeed Ali, Ali, Haider, Alruwaythi, Maryam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166990/
https://www.ncbi.nlm.nih.gov/pubmed/35669665
http://dx.doi.org/10.1155/2022/8622022
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author Saba, Tanzila
Khan, Amjad Rehman
Abunadi, Ibrahim
Bahaj, Saeed Ali
Ali, Haider
Alruwaythi, Maryam
author_facet Saba, Tanzila
Khan, Amjad Rehman
Abunadi, Ibrahim
Bahaj, Saeed Ali
Ali, Haider
Alruwaythi, Maryam
author_sort Saba, Tanzila
collection PubMed
description Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depressive symptoms using speech analysis. This study presents a hybrid model to identify and predict mental illness from Arabic speech analysis due to depression. The proposed hybrid model comprises convolutional neural network (CNN) and a support vector machine (SVM) to identify and predict mental disorders. Experiments are performed on the Arabic speech benchmark data set of 200 speeches. A total of 70% of data were reserved for training, while 30% of data were to test the proposed model. The hybrid model (CNN + SVM) attained a 90.0% and 91.60% accuracy rate to predict the depression from Arabic speech analysis for training and testing stages. To authenticate the results of a proposed hybrid model, recurrent neural network (RNN) and CNN are also applied to the same data set individually, and the results are compared with each other. The RNN achieved an 80.70% and 81.60% accuracy rate to predict depression while speaking in the training and testing stages. The CNN predicted the depression in the training and testing stages with 88.50% and 86.60% accuracy rates. Based on the analysis, the proposed hybrid model secured better prediction results than individual RNN and CNN models on the same data set. Furthermore, the suggested model had a lower FPR, FNR, and higher accuracy, AUC, sensitivity, and specificity rate than individual RNN, CNN model performance in predicting depression. Finally, the achieved findings will be helpful to classify depression while speaking Arabic/speech and will be beneficial for physicians, psychiatrists, and psychologists in the detection of depression.
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spelling pubmed-91669902022-06-05 Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning Saba, Tanzila Khan, Amjad Rehman Abunadi, Ibrahim Bahaj, Saeed Ali Ali, Haider Alruwaythi, Maryam Comput Intell Neurosci Research Article Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depressive symptoms using speech analysis. This study presents a hybrid model to identify and predict mental illness from Arabic speech analysis due to depression. The proposed hybrid model comprises convolutional neural network (CNN) and a support vector machine (SVM) to identify and predict mental disorders. Experiments are performed on the Arabic speech benchmark data set of 200 speeches. A total of 70% of data were reserved for training, while 30% of data were to test the proposed model. The hybrid model (CNN + SVM) attained a 90.0% and 91.60% accuracy rate to predict the depression from Arabic speech analysis for training and testing stages. To authenticate the results of a proposed hybrid model, recurrent neural network (RNN) and CNN are also applied to the same data set individually, and the results are compared with each other. The RNN achieved an 80.70% and 81.60% accuracy rate to predict depression while speaking in the training and testing stages. The CNN predicted the depression in the training and testing stages with 88.50% and 86.60% accuracy rates. Based on the analysis, the proposed hybrid model secured better prediction results than individual RNN and CNN models on the same data set. Furthermore, the suggested model had a lower FPR, FNR, and higher accuracy, AUC, sensitivity, and specificity rate than individual RNN, CNN model performance in predicting depression. Finally, the achieved findings will be helpful to classify depression while speaking Arabic/speech and will be beneficial for physicians, psychiatrists, and psychologists in the detection of depression. Hindawi 2022-05-27 /pmc/articles/PMC9166990/ /pubmed/35669665 http://dx.doi.org/10.1155/2022/8622022 Text en Copyright © 2022 Tanzila Saba et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Saba, Tanzila
Khan, Amjad Rehman
Abunadi, Ibrahim
Bahaj, Saeed Ali
Ali, Haider
Alruwaythi, Maryam
Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title_full Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title_fullStr Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title_full_unstemmed Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title_short Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning
title_sort arabic speech analysis for classification and prediction of mental illness due to depression using deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166990/
https://www.ncbi.nlm.nih.gov/pubmed/35669665
http://dx.doi.org/10.1155/2022/8622022
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