Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review

Bahbibi Rahmatullah

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Type :	Article
Subject :	T Technology (General)
ISBN :	2089-3191
Main Author :	Bahbibi Rahmatullah
Additional Authors :	Shamsul Arrieya Ariffin Azniah Ismail
Title :	Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review
Hits :	239

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	Bulletin of Electrical Engineering and Informatics
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support. _ 2024, Institute of Advanced Engineering and Science. All rights reserved.

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