BXCNN: A novel depression detection model

Research Article
Open access

BXCNN: A novel depression detection model

Haocheng Xi 1* , Haoyang Zhong 2 , Yutong Wang 3
  • 1 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • 2 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • 3 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • *corresponding author shaunspike813jelly@gmail.com
ACE Vol.68
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-457-6
ISBN (Online): 978-1-83558-458-3

Abstract

In response to the mounting societal pressures, an increasing number of individuals grappling with mental health challenges are turning to social media platforms to express their feelings. The utilization of deep learning models for analyzing social media data has become increasingly crucial in detecting early signs of depression. Early intervention through depression detection can significantly enhance patients’ quality of life and even save lives. However, many existing deep learning models suffer from low prediction accuracy, exacerbated by the imbalance between positive and negative samples in the collected data. To address these challenges, we propose a novel depression detection model integrated with BERT, XGBoost, and Convolutional Neural Networks (BXCNN). This model harnesses the advantages of ensemble learning and deep learning technologies by integrating XGBoost for feature extraction to alleviate data imbalance and CNN for classification. We transform depression-related textual data into sentence vectors using BERT to capture semantic information effectively. These features are then fed into a CNN classifier to accurately predict the likelihood of individuals exhibiting depressive symptoms. Through empirical evaluations on relevant datasets, our approach excels across various evaluation metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC).

Keywords:

Early depression detection, Ensemble model, Convolutional neural network (CNN), Data imbalance

Xi,H.;Zhong,H.;Wang,Y. (2024). BXCNN: A novel depression detection model. Applied and Computational Engineering,68,190-202.
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References

[1]. Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET), pages 1–6. Ieee, 2017.

[2]. Vanishri Arun, V Prajwal, Murali Krishna, BV Arunkumar, SK Padma, and V Shyam. A boosted machine learning approach for detection of depression. In 2018 IEEE symposium series on computational intelligence (SSCI), pages 41–47. IEEE, 2018.

[3]. Fidel Cacheda, Diego Fernandez, Francisco J Novoa, and Victor Carneiro. Early detection of depression: social network analysis and random forest techniques. Journal of medical Internet research, 21(6):e12554, 2019.

[4]. Fidel Cacheda, Diego Fern´andez Iglesias, Francisco Javier N´ovoa, and Victor Carneiro. Analysis and experiments on early detection of depression. CLEF (Working Notes), 2125:43, 2018.

[5]. Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.

[6]. Qing Cong, Zhiyong Feng, Fang Li, Yang Xiang, Guozheng Rao, and Cui Tao. Xa-bilstm: a deep learning approach for depression detection in imbalanced data. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM), pages 1624–1627. IEEE, 2018.

[7]. Rula Kamil and Ayad R Abbas. Predicating depression on twitter using hybrid model bilstm-xgboost. Bulletin of Electrical Engineering and Informatics, 12(6):3620–3627, 2023.

[8]. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.

[9]. Liuwu Li, Runwei Situ, Junyan Gao, Zhenguo Yang, and Wenyin Liu. A hybrid model combining convolutional neural network with xgboost for predicting social media popularity. In Proceedings of the 25th ACM international conference on Multimedia, pages 1912–1917, 2017.

[10]. Zhenyu Liu, Dongyu Wang, Lan Zhang, and Bin Hu. A novel decision tree for depression recognition in speech. arXiv preprint arXiv:2002.12759, 2020.

[11]. David E Losada, Fabio Crestani, and Javier Parapar. erisk 2017: Clef lab on early risk prediction on the internet: experimental foundations. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings 8, pages 346–360. Springer, 2017.

[12]. Minsu Park, Chiyoung Cha, and Meeyoung Cha. Depressive moods of users portrayed in twitter. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2012, pages 1–8, 2012.

[13]. Giovanni Puccetti, Alessio Miaschi, and Felice Dell’Orletta. How do bert embeddings organize linguistic knowledge?In Proceedings of deep learning inside out (DeeLIO): the 2nd workshop on knowledge extraction and integration for deep learning architectures, pages 48–57, 2021.

[14]. [14] Andrew G Reece, Andrew J Reagan, Katharina LM Lix, Peter Sheridan Dodds, Christopher M Danforth, and Ellen J Langer. Forecasting the onset and course of mental illness with twitter data. Scientific reports, 7(1):13006, 2017.

[15]. Farig Sadeque, Dongfang Xu, and Steven Bethard. Uarizona at the clef erisk 2017 pilot task: linear and recurrent models for early depression detection. In CEUR workshop proceedings, volume 1866. NIH Public Access, 2017.

[16]. Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin, and Ivan V Smirnov. Feature engineering for depression detection in social media. In ICPRAM, pages 426–431, 2018.

[17]. Setthanun Thongsuwan, Saichon Jaiyen, Anantachai Padcharoen, and Praveen Agarwal. Convxgb: A new deep learning model for classification problems based on cnn and xgboost. Nuclear Engineering and Technology, 53(2):522–531, 2021.

[18]. Christiana Sri Wahyuningsih, Achmad Arman Subijanto, and Bhisma Murti. Logistic regression on factors affecting depression among the elderly. Journal of Epidemiology and Public Health, 4(3):171–179, 2019.

[19]. Ronghua Xu and Qingpeng Zhang. Understanding online health groups for depression: social network and linguistic perspectives. Journal of medical Internet research, 18(3):e63, 2016.

[20]. Jingwen Zhang, Haochen Yin, Jinfang Wang, Shuxin Luan, and Chang Liu. Severe major depression disorders detection using adaboost-collaborative representation classification method. In 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), pages 584–588. IEEE, 2018.

Cite this article

Xi,H.;Zhong,H.;Wang,Y. (2024). BXCNN: A novel depression detection model. Applied and Computational Engineering,68,190-202.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

ISBN：978-1-83558-457-6(Print) / 978-1-83558-458-3(Online)
Editor：Alan Wang, Roman Bauer
Conference website: https://www.confcds.org/
Conference date: 12 September 2024
Series: Applied and Computational Engineering
Volume number: Vol.68
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET), pages 1–6. Ieee, 2017.

[2]. Vanishri Arun, V Prajwal, Murali Krishna, BV Arunkumar, SK Padma, and V Shyam. A boosted machine learning approach for detection of depression. In 2018 IEEE symposium series on computational intelligence (SSCI), pages 41–47. IEEE, 2018.

[3]. Fidel Cacheda, Diego Fernandez, Francisco J Novoa, and Victor Carneiro. Early detection of depression: social network analysis and random forest techniques. Journal of medical Internet research, 21(6):e12554, 2019.

[4]. Fidel Cacheda, Diego Fern´andez Iglesias, Francisco Javier N´ovoa, and Victor Carneiro. Analysis and experiments on early detection of depression. CLEF (Working Notes), 2125:43, 2018.

[5]. Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.

[6]. Qing Cong, Zhiyong Feng, Fang Li, Yang Xiang, Guozheng Rao, and Cui Tao. Xa-bilstm: a deep learning approach for depression detection in imbalanced data. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM), pages 1624–1627. IEEE, 2018.

[7]. Rula Kamil and Ayad R Abbas. Predicating depression on twitter using hybrid model bilstm-xgboost. Bulletin of Electrical Engineering and Informatics, 12(6):3620–3627, 2023.

[8]. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.

[9]. Liuwu Li, Runwei Situ, Junyan Gao, Zhenguo Yang, and Wenyin Liu. A hybrid model combining convolutional neural network with xgboost for predicting social media popularity. In Proceedings of the 25th ACM international conference on Multimedia, pages 1912–1917, 2017.

[10]. Zhenyu Liu, Dongyu Wang, Lan Zhang, and Bin Hu. A novel decision tree for depression recognition in speech. arXiv preprint arXiv:2002.12759, 2020.

[11]. David E Losada, Fabio Crestani, and Javier Parapar. erisk 2017: Clef lab on early risk prediction on the internet: experimental foundations. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings 8, pages 346–360. Springer, 2017.

[12]. Minsu Park, Chiyoung Cha, and Meeyoung Cha. Depressive moods of users portrayed in twitter. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2012, pages 1–8, 2012.

[13]. Giovanni Puccetti, Alessio Miaschi, and Felice Dell’Orletta. How do bert embeddings organize linguistic knowledge?In Proceedings of deep learning inside out (DeeLIO): the 2nd workshop on knowledge extraction and integration for deep learning architectures, pages 48–57, 2021.

[14]. [14] Andrew G Reece, Andrew J Reagan, Katharina LM Lix, Peter Sheridan Dodds, Christopher M Danforth, and Ellen J Langer. Forecasting the onset and course of mental illness with twitter data. Scientific reports, 7(1):13006, 2017.

[15]. Farig Sadeque, Dongfang Xu, and Steven Bethard. Uarizona at the clef erisk 2017 pilot task: linear and recurrent models for early depression detection. In CEUR workshop proceedings, volume 1866. NIH Public Access, 2017.

[16]. Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin, and Ivan V Smirnov. Feature engineering for depression detection in social media. In ICPRAM, pages 426–431, 2018.

[17]. Setthanun Thongsuwan, Saichon Jaiyen, Anantachai Padcharoen, and Praveen Agarwal. Convxgb: A new deep learning model for classification problems based on cnn and xgboost. Nuclear Engineering and Technology, 53(2):522–531, 2021.

[18]. Christiana Sri Wahyuningsih, Achmad Arman Subijanto, and Bhisma Murti. Logistic regression on factors affecting depression among the elderly. Journal of Epidemiology and Public Health, 4(3):171–179, 2019.

[19]. Ronghua Xu and Qingpeng Zhang. Understanding online health groups for depression: social network and linguistic perspectives. Journal of medical Internet research, 18(3):e63, 2016.

[20]. Jingwen Zhang, Haochen Yin, Jinfang Wang, Shuxin Luan, and Chang Liu. Severe major depression disorders detection using adaboost-collaborative representation classification method. In 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), pages 584–588. IEEE, 2018.

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