Applying Deep Learning Technique for Depression Classification in Social Media Text

Ahmad, Hussain; Asghar, Muhammad Zubair; Alotaibi, Fahad Mazaed; Hameed, Ibrahim A.

doi:10.1166/jmihi.2020.3169

Cited by 24 publications

(7 citation statements)

References 12 publications

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“…where N represents the number of pixels in total in an image; the mean of an image pixel values is represented by p. The calculation of energy, correlation, entropy, contrast, and homogeneity has been done in equations ( 11)- (15), respectively.…”

Section: Preprocessingmentioning

confidence: 99%

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An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms

Fayaz

Qureshi

Kussainova

et al. 2021

Computational and Mathematical Methods in Medicine

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In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In the preprocessing stage, the median filter has been used in order to remove salt-and-pepper noise because MRI images are normally affected by this type of noise, the grayscale images are also converted to RGB images in this stage. In the preprocessing stage, the histogram equalization has also been used to enhance the quality of each RGB channel. In the feature extraction stage, the three channels, namely, red, green, and blue, are extracted from the RGB images and statistical measures, namely, mean, variance, skewness, kurtosis, entropy, energy, contrast, homogeneity, and correlation, are calculated for each channel; hence, a total of 27 features, 9 for each channel, are extracted from an RGB image. After the feature extraction stage, different machine learning algorithms, such as artificial neural network, k -nearest neighbors’ algorithm, decision tree, and Naïve Bayes classifiers, have been applied in the classification stage on the features extracted in the feature extraction stage. We recorded the results with all these algorithms and found that the decision tree results are better as compared to the other classification algorithms which are applied on these features. Hence, we have considered decision tree for further processing. We have also compared the results of the proposed method with some well-known algorithms in terms of simplicity and accuracy; it was noted that the proposed method outshines the existing methods.

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Section: Preprocessingmentioning

confidence: 99%

“…The results indicate that this method provides good results. Similarly, another method has been proposed by Hussain et al [ 15 ] for depression classification in social media by using deep learning method.…”

Section: Introductionmentioning

confidence: 99%

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms

Fayaz

Qureshi

Kussainova

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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“…The updated version of the Bat algorithm can also benefit medical image classification [ 20 ]. Since animal unstructured text data can be collected from Twitter, supervised machine learning algorithms such as deep neural networks can recognize online individuals suffering from depression [ 21 ]. Particle Swarm Optimization (PSO) is a swarm-based smart stochastic optimization approach inspired by the natural way bees swarm when looking for food.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

115

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SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient’s computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

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“…In recent years, social media has emerged as a valuable data source for health informatics [ 18 ]. Data from online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of health textual content which can be utilized to tackle various medical tasks such as psychopathic class detection [ 19 , 20 ], depression classification [ 21 ], disease detection [ 22 ], and adverse drug reaction detection [ 23 ]. It is the development of Web 2.0 and Health 2.0 that makes a great deal of health-related informative contents available.…”

Section: Introductionmentioning

confidence: 99%

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Yahya

Asiri

Alyami

2022

Computational and Mathematical Methods in Medicine

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Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

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Applying Deep Learning Technique for Depression Classification in Social Media Text

Cited by 24 publications

References 12 publications

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

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