A big data application to predict depression in the university based on the reading habits

Hou, Yujiao; Xu, Jingjing; Huang, Yixin; Ma, Xiaofeng

doi:10.1109/icsai.2016.7811112

Cited by 29 publications

(13 citation statements)

References 9 publications

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“…In another study, Hou et al [ 59 ] analyzed reading habits in addition to social data usage in order to predict depressive tendencies among students. The researchers measured three dimensions: reading times, reading frequency, and reading span (at most five types of books and periodicals).…”

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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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

View full text Add to dashboard Cite

show abstract

“…For performing linear classification, a hyperplane is formed, which separates the two classes well enough. For non-linear classification, kernel methods like Gaussian kernel, Laplace kernel, Polynomial kernel are used [40], [41].…”

Section: Ensemble Classifier Modelmentioning

confidence: 99%

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

et al. 2020

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Major depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers. INDEX TERMS K-nearest neighbors, major depressive disorder, multilayer perceptron, random forest, random forest-based feature elimination, stacking generalization and support vector machine.

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“…Suicide prediction among twitter users showed that CNN scored a higher accuracy rate, 78%, outperforming support vector machine (SVM), [12]. Machine learning classifiers can predict which people are struggling with anxiety based on their reading habits and the model reported with best prediction was Naive Bayes, [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology

Alharthi

2020

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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Introduction: Emerging reports indicate heightened anxiety among university students during the Corona pandemic. Implications of which can impact their academic performance. Artificial intelligence (AI) through machine learning can be used to predict which students are more susceptible to anxiety which can inform closer monitoring and early intervention. To date, there are no studies that have explored the efficacy of AI to predict anxiety among college students. Objective: to develop the best fit model to predict anxiety and to rank the most important factors affecting anxiety. Method: Data was collected using an online survey that included general information; Covid-19 stressors and (GAD-7). This scale categorizes level of anxiety to none, mild, moderate, and severe. We received 917 survey answers. Several machine learning classifiers were used to develop the best fit model to predict student level of anxiety. Results: the best performance based on AUC is AdaBoost (0.943) followed by neural network (0.936). Highest accuracy and F1 were for neural network (0.754) and (0.749) respectively, then neural network selected to be the best fit model. The three scoring methods revealed that the top three features that predicted anxiety to be gender; sufficient support from family and friends; and fixed family income. Conclusion: Neural network model can assist college counselors to predict which students are going through anxiety and revealed the top three features for heightened student anxiety to be gender, a support system, and family fixed income. This information can alter college councilors for early mental intervention.

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A big data application to predict depression in the university based on the reading habits

Cited by 29 publications

References 9 publications

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

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

Realizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults

Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology

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