A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh

Jasiya Fairiz Raisa; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-3-031-15037-1_20

A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh

Jasiya Fairiz Raisa^*
, M. Shamim Kaiser
, Mufti Mahmud

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Postpartum depression is a severe mental health issue exhibited among perinatal women after the childbirth process. While the negative impact of postpartum depression is extensive in developing countries, there is a significant lack of proper tools and techniques to predict the disorder due to negligence. This work proposes a machine learning-based system for finding the risk factors and prevalence of postpartum depression in Bangladesh. We developed a survey of different socio-demographic questions and modified questions from two standard postpartum depression screening scales (EPDS, PHQ-2). Data from 150 women have been collected, processed, and implemented in different machine learning models to find—the best performing models. Based on the collected data of the perinatal women in Bangladesh, the best performing machine learning model was Random Forest. The performance metrics for the best model were AUC: 98%, Accuracy: 89%, and Sensitivity: 89%. The performance of the models varies from 88%–98% (AUC), 82%–89% (Accuracy), and 81%–89% (Sensitivity). We have also found the top risk factors for causing PPD. According to this work, the prevalence of PPD in Bangladesh is 66.7% (Considering the medium and high chance of PPD). This proposed work is the first to detect the risk factors and prevalence of PPD in Bangladesh using a machine learning approach.

Original language	English
Title of host publication	Brain Informatics - 15th International Conference, BI 2022, Proceedings
Editors	Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	241-252
Number of pages	12
ISBN (Print)	9783031150364
DOIs	https://doi.org/10.1007/978-3-031-15037-1_20
State	Published - 2022
Externally published	Yes
Event	15th International Conference on Brain Informatics, BI 2022 - Virtual, Online Duration: 15 Jul 2022 → 17 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13406 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Brain Informatics, BI 2022
City	Virtual, Online
Period	15/07/22 → 17/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Depression
Detection model
Machine learning
Mental health
Postpartum depression

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-15037-1_20

Cite this

Raisa, J. F., Kaiser, M. S., & Mahmud, M. (2022). A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. In M. Mahmud, J. He, S. Vassanelli, A. van Zundert, & N. Zhong (Eds.), Brain Informatics - 15th International Conference, BI 2022, Proceedings (pp. 241-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-15037-1_20

Raisa, Jasiya Fairiz ; Kaiser, M. Shamim ; Mahmud, Mufti. / A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. Brain Informatics - 15th International Conference, BI 2022, Proceedings. editor / Mufti Mahmud ; Jing He ; Stefano Vassanelli ; André van Zundert ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. pp. 241-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d36b0d591ce34d538778b5bb690042fb,

title = "A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh",

abstract = "Postpartum depression is a severe mental health issue exhibited among perinatal women after the childbirth process. While the negative impact of postpartum depression is extensive in developing countries, there is a significant lack of proper tools and techniques to predict the disorder due to negligence. This work proposes a machine learning-based system for finding the risk factors and prevalence of postpartum depression in Bangladesh. We developed a survey of different socio-demographic questions and modified questions from two standard postpartum depression screening scales (EPDS, PHQ-2). Data from 150 women have been collected, processed, and implemented in different machine learning models to find—the best performing models. Based on the collected data of the perinatal women in Bangladesh, the best performing machine learning model was Random Forest. The performance metrics for the best model were AUC: 98\%, Accuracy: 89\%, and Sensitivity: 89\%. The performance of the models varies from 88\%–98\% (AUC), 82\%–89\% (Accuracy), and 81\%–89\% (Sensitivity). We have also found the top risk factors for causing PPD. According to this work, the prevalence of PPD in Bangladesh is 66.7\% (Considering the medium and high chance of PPD). This proposed work is the first to detect the risk factors and prevalence of PPD in Bangladesh using a machine learning approach.",

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Raisa, JF, Kaiser, MS & Mahmud, M 2022, A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. in M Mahmud, J He, S Vassanelli, A van Zundert & N Zhong (eds), Brain Informatics - 15th International Conference, BI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13406 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 241-252, 15th International Conference on Brain Informatics, BI 2022, Virtual, Online, 15/07/22. https://doi.org/10.1007/978-3-031-15037-1_20

A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. / Raisa, Jasiya Fairiz; Kaiser, M. Shamim; Mahmud, Mufti.
Brain Informatics - 15th International Conference, BI 2022, Proceedings. ed. / Mufti Mahmud; Jing He; Stefano Vassanelli; André van Zundert; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. p. 241-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Raisa, Jasiya Fairiz

AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

PY - 2022

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N2 - Postpartum depression is a severe mental health issue exhibited among perinatal women after the childbirth process. While the negative impact of postpartum depression is extensive in developing countries, there is a significant lack of proper tools and techniques to predict the disorder due to negligence. This work proposes a machine learning-based system for finding the risk factors and prevalence of postpartum depression in Bangladesh. We developed a survey of different socio-demographic questions and modified questions from two standard postpartum depression screening scales (EPDS, PHQ-2). Data from 150 women have been collected, processed, and implemented in different machine learning models to find—the best performing models. Based on the collected data of the perinatal women in Bangladesh, the best performing machine learning model was Random Forest. The performance metrics for the best model were AUC: 98%, Accuracy: 89%, and Sensitivity: 89%. The performance of the models varies from 88%–98% (AUC), 82%–89% (Accuracy), and 81%–89% (Sensitivity). We have also found the top risk factors for causing PPD. According to this work, the prevalence of PPD in Bangladesh is 66.7% (Considering the medium and high chance of PPD). This proposed work is the first to detect the risk factors and prevalence of PPD in Bangladesh using a machine learning approach.

AB - Postpartum depression is a severe mental health issue exhibited among perinatal women after the childbirth process. While the negative impact of postpartum depression is extensive in developing countries, there is a significant lack of proper tools and techniques to predict the disorder due to negligence. This work proposes a machine learning-based system for finding the risk factors and prevalence of postpartum depression in Bangladesh. We developed a survey of different socio-demographic questions and modified questions from two standard postpartum depression screening scales (EPDS, PHQ-2). Data from 150 women have been collected, processed, and implemented in different machine learning models to find—the best performing models. Based on the collected data of the perinatal women in Bangladesh, the best performing machine learning model was Random Forest. The performance metrics for the best model were AUC: 98%, Accuracy: 89%, and Sensitivity: 89%. The performance of the models varies from 88%–98% (AUC), 82%–89% (Accuracy), and 81%–89% (Sensitivity). We have also found the top risk factors for causing PPD. According to this work, the prevalence of PPD in Bangladesh is 66.7% (Considering the medium and high chance of PPD). This proposed work is the first to detect the risk factors and prevalence of PPD in Bangladesh using a machine learning approach.

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Raisa JF, Kaiser MS, Mahmud M. A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. In Mahmud M, He J, Vassanelli S, van Zundert A, Zhong N, editors, Brain Informatics - 15th International Conference, BI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 241-252. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-15037-1_20