Hysterectomy as a predictor of depression: A comprehensive analysis using logistic regression and machine learning

Area of Science:

• Medical research
• Public health
• Epidemiology

Background:

• Growing evidence links hysterectomy to depression.
• Patient mental health impact is significant and requires attention.

Purpose of the Study:

• To investigate the correlation between hysterectomy and depression.
• Utilize the National Health Interview Survey (NHIS) database for analysis.

Main Methods:

• Analysis of NHIS 2023 data.
• Logistic regression models to assess correlation.
• ROC curve and KNN machine learning for predictive value.

Main Results:

• 14,327 participants analyzed; 671 had both hysterectomy and depression.
• Hysterectomy identified as a significant risk factor for depression (OR=1.32).
• ROC curve (AUC=0.797) and KNN analysis showed predictive value.

Conclusions:

• A robust correlation between hysterectomy and depression is confirmed.
• Findings provide valuable insights for future research and clinical practice.