Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 21, 2025Advances in Multidisciplinary & Scientific Research Journal PublicationOpen Access

An Enhanced Framework for Postpartum Depression Diagnosis

View Full Paper
Ask AI
Bookmark
Share

Authors

HOH.M. OsaretinKOK.O. Obahiagbon

Discussion

Loading...

Member takes

Overview

Quasi-experimental analysis shows high accuracy in diagnosing postpartum depression using AI tools.

Key Points

  • The neural network achieved a 0.98% accuracy level in diagnosing postpartum depression after training.
  • Training data included a dataset of 1500 symptoms used to predict different types of postpartum depression.
  • Quasi-experimental methodology was utilized, employing Python and Anaconda for analysis.
  • Further exploration of artificial intelligence tools is necessary to improve diagnosis of postpartum depression.

Cite This Study

Osaretin et al. (2025) studied this question.

synapsesocial.com/papers/68d46ac231b076d99fa6832bhttps://doi.org/10.22624/aims/sij/v11n3p1
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Clinically Practical Postpartum Depression Predictor: Machine Learning Model Based on Simplified Indicators2025
  2. 2A Comprehensive Study on Postpartum Depression Prediction Using Machine Learning Approaches2025
  3. 3Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review2025
  4. 4Predicting Postpartum Depression Risk Using Social Determinants of Health.2025
  5. 5Development and validation of an interpretable machine learning model and online web-based calculator based on social-ecosystem theory for early prediction of postpartum depression: a longitudinal study2025 · 6 citations