DJCCMT

Delta Journal of Computing, Communications and Media Technologies

ISSN:3092-8478

Advancing research and innovation at the intersection of computing technology and media. A publication of Southern Delta University, Ozoro.

Delta Journal of Computing, Communications and Media Technologies(DJCCMT) is an open access double-blind peer reviewed and refereed Journal that brings together reasoned thoughts, research, and industry practice in areas of Computing, Artificial Intelligence, Robotics, System Engineering, Data Science, Analytics, Embedded Systems, Information and System Security, Media Studies, Communication Technologies, Information Science, Library Science, Educational Technologies, Applied Computing, and related disciplines in a reader-friendly format. The Journal is published online monthly with print version issue in February, May, August and November.

Delta Journal of Computing, Communications and Media Technologies

Volume 1 · Issue 1 · December 2024
Title of Paper

PEOPLE’S WELLBEING IN HEALTHCARE USING PREDICTIVE MODELING: A FOCUS ON POSTPARTUM DEPRESSION

Abstract

Postpartum depression (PPD) affects up to one in seven women in the first year after childbirth, yet early identification remains a challenge. This study evaluates the application of predictive modelling techniques in healthcare to enhance our understanding of PPD and to support timely intervention. We performed a comprehensive review of clinical and demographic risk factors, such as antenatal mood disorders, social support, obstetric complications, and hormonal changes, and assembled a dataset integrating these variables. Using machine-learning algorithms (e.g., logistic regression, random forests), we developed and validated a predictive model to estimate individual PPD risk. Model performance was assessed via cross-validation, reporting accuracy, sensitivity, and area under the ROC curve. Our results demonstrate that a multivariable predictive approach can reliably stratify women by PPD risk, with the best model achieving an AUC of 0.87. By pinpointing high-risk individuals before symptom onset, this framework offers a pathway for targeted screening and personalized care. Findings inform clinicians, researchers, and policymakers on the promise of predictive analytics to improve maternal mental health outcomes.

Authors

Umukoro Ighofewo Isaac, Daniel Ukpenusiowho

Keywords

Postpartum, Depression, Wellbeing, Modelling, Predictive analytics

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