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Updated: Sep 10, 2025

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

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Constructing a predictive model for acute mastitis in lactating women based on machine learning.

Liujing Zhu¹, Zuyan Huang², Yan Chen³

¹Department of Galactophore, Liuzhou Hospital, Guangzhou Women and Children's Medical Center, Liuzhou, Guangxi, China.

Scientific Reports

|August 22, 2025

Summary

Machine learning models accurately predict acute lactational mastitis risk in women. Key indicators like age, cracked nipples, CRP, neutrophils, and WBCs help identify high-risk individuals for timely intervention.

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Last Updated: Sep 10, 2025

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Area of Science:

Medical Informatics
Women's Health
Infectious Diseases

Background:

Acute lactational mastitis is a common complication affecting lactating women, with complex etiology and non-specific early symptoms.
Delayed diagnosis can lead to severe infections and prolonged recovery, highlighting the need for better risk identification.
Existing research on risk factors for lactational mastitis is incomplete.

Purpose of the Study:

To develop and validate a predictive model for acute lactational mastitis risk in lactating women using machine learning.
To identify key risk factors influencing the occurrence of acute lactational mastitis.
To provide a tool for timely clinical intervention and accurate diagnosis.

Main Methods:

A retrospective case-control study involving 369 patients with acute mastitis and 447 healthy controls.
Data collected included patient demographics and clinical indicators such as age, parity, and C-reactive protein (CRP).
Machine learning algorithms (Logistic Regression, Naive Bayes, XGBoost, Multilayer Perceptron) were used to build and test predictive models.

Main Results:

The Multilayer Perceptron (MLP) model demonstrated superior performance with an AUROC of 0.898, accuracy of 0.840, sensitivity of 0.820, and specificity of 0.863.
Five indicators were found to be significantly associated with acute lactational mastitis: age, cracked nipples, CRP, neutrophils (NE), and white blood cells (WBC).
Decision Curve Analysis confirmed the clinical utility of the MLP model across various threshold ranges.

Conclusions:

A robust predictive model for acute lactational mastitis has been successfully developed using machine learning.
Key risk factors including age, cracked nipples, CRP, NE, and WBC levels are crucial for predicting mastitis occurrence.
The developed model and identified factors offer valuable insights for early detection and targeted management of lactational mastitis.