Dietary patterns and chronic prostatitis: a symptom severity prediction model based on nutritional clustering and machine learning.

ABSTRACT:

Background

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) has a multifactorial etiology where diet is considered an important factor. This study aimed to develop a predictive model for CP/CPPS symptom severity by analyzing food frequency questionnaire (FFQ) data with machine learning techniques, providing a basis for personalized nutritional interventions.

Methods

This study included 313 patients with CP/CPPS. We used principal component analysis (PCA) to extract dietary patterns from FFQ data and applied LASSO regression to select key predictors of symptom severity. Subsequently, six machine learning models (logistic regression, random forest, XGBoost, support vector machine, K-nearest neighbors, and multilayer perceptron) were trained and compared. Model perfor

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12863200 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature