Predicting postpartum suicide: a particle swarm optimization approach

Yuchen Zhu; Yujie He

doi:10.1117/12.3039343

4 September 2024 Predicting postpartum suicide: a particle swarm optimization approach

Yuchen Zhu, Yujie He

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132591B (2024) https://doi.org/10.1117/12.3039343
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

Predicting postpartum suicide tendencies is crucial for maternal health and public safety. This study compares four machine learning models for predicting these tendencies: Particle Swarm Optimization with Random Forest (PSO-RF), Random Forest, Naive Bayes, and Logistic Regression. PSO-RF outperformed the other models, achieving an accuracy of 0.991, perfect precision, high recall, and an ROC_AUC of 0.999. Random Forest also performed well, but with a slightly lower recall. Naive Bayes and Logistic Regression had lower accuracy and ROC_AUC scores, indicating less reliable predictions. Overall, the study suggests that PSO-RF is the most effective model for predicting postpartum suicide tendencies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuchen Zhu and Yujie He "Predicting postpartum suicide: a particle swarm optimization approach", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132591B (4 September 2024); https://doi.org/10.1117/12.3039343

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