A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

Chiong, Raymond; Budhi, Gregorius Satia; Dhakal, Sandeep; Chiong, Fabian

doi:10.1016/j.compbiomed.2021.104499

Cited by 113 publications

(34 citation statements)

References 47 publications

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“…Machine learning is increasingly used in depression [ 13 – 15 ]. Compared with human experts, the amount of data, computational complexity, and storage capacity of medical decision support systems are relatively high [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting depression among rural and urban disabled elderly in China using a random forest classifier

Ren

2022

BMC Psychiatry

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With global aging, the number of elderly with physical disabilities is also increasing. Compared with the ordinary elderly, the elderly who lose their independence are more likely to have the symptoms of depression. Reducing depression may help to alleviate the disability process of those who find themselves in the disabled stages. Therefore, the purpose of this study is to explore the predictive effects of demographic characteristics, health behavior, health status, family relations, social relations, and subjective attitude on depression in rural and urban disabled elderly to improve early depression symptom recognition.A total of 1460 older adults aged 60 and disabled were selected from China Family Panel Studies (CFPS). Depression was assessed according to The Center for Epidemiologic Studies Depression Scale (CES-D). This paper used the random forest classifier to predict the depression of the disabled elderly from six aspects: demographic characteristics, health status, health behavior, family relationship, and social relationship. The prediction model was established based on 70% of the training set and 30% of the test set. The depression rate of rural disabled elderly was 57.67%, and that of urban disabled elderly was 44.59%. The mean values of the 10-k cross-validated results were 0.71 in rural areas and 0.70 in urban areas. AUC:0.71, specificity: 65.3%, sensitivity: 80.6% for rural disabled elderly with depression; AUC:0.78, specificity: 78.1%, sensitivity: 64.2% for urban disabled elderly with depression, respectively. There are apparent differences in the top ten predictors between rural and urban disabled elderly. The common predictors were self-rated health, changing in perceived health, disease or accidence experience within the past 2 weeks, life satisfaction, trusting people, BMI, and having trust in the future. Non-common predictors were chronic diseases, neighborly relations, total medical expenses within 1 year, community emotion, sleep duration, and family per capita income. Using random forest data to predict the depression of the disabled elderly may lead to early detection of depression.

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Section: Introductionmentioning

confidence: 99%

Predicting depression among rural and urban disabled elderly in China using a random forest classifier

Ren

2022

BMC Psychiatry

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“…SVMs, founded on the structural risk minimisation principle and statistical learning theory [ 48 ], have been widely used in many real-world applications and have displayed satisfactory performance (e.g., see [ 49 – 51 ]). Given n training samples , the standard form of ε -SVM regression can be expressed as Eq (2) .…”

Section: Methodsmentioning

confidence: 99%

Body fat prediction through feature extraction based on anthropometric and laboratory measurements

Fan

Chiong

et al. 2022

PLoS ONE

Self Cite

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Obesity, associated with having excess body fat, is a critical public health problem that can cause serious diseases. Although a range of techniques for body fat estimation have been developed to assess obesity, these typically involve high-cost tests requiring special equipment. Thus, the accurate prediction of body fat percentage based on easily accessed body measurements is important for assessing obesity and its related diseases. By considering the characteristics of different features (e.g. body measurements), this study investigates the effectiveness of feature extraction for body fat prediction. It evaluates the performance of three feature extraction approaches by comparing four well-known prediction models. Experimental results based on two real-world body fat datasets show that the prediction models perform better on incorporating feature extraction for body fat prediction, in terms of the mean absolute error, standard deviation, root mean square error and robustness. These results confirm that feature extraction is an effective pre-processing step for predicting body fat. In addition, statistical analysis confirms that feature extraction significantly improves the performance of prediction methods. Moreover, the increase in the number of extracted features results in further, albeit slight, improvements to the prediction models. The findings of this study provide a baseline for future research in related areas.

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“…Depressed participants were found to have a higher ratio of negative words and more frequent tweets and retweets [ 55 ]. Chiong et al [ 56 ] aimed to combine several social media platforms and examine whether ML algorithm will predict depression, even when the posted text does not explicitly contain specific keywords such as ‘depression’ or ‘diagnosis’. The researchers tested linguistic features of two public Twitter datasets to train and test the ML models, and then another three non-Twitter depression-diagnosis-only datasets (sourced from Facebook, Reddit, and an electronic diary) to test the performance of the trained models against other social media sources.…”

Section: Data Miningmentioning

confidence: 99%

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Richter

Fishbain

Richter‐Levin

et al. 2021

JPM

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The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

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A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

Cited by 113 publications

References 47 publications

Predicting depression among rural and urban disabled elderly in China using a random forest classifier

Predicting depression among rural and urban disabled elderly in China using a random forest classifier

Body fat prediction through feature extraction based on anthropometric and laboratory measurements

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

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