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

## TL;DR

This study uses machine learning to predict symptom severity of chronic prostatitis based on dietary patterns, highlighting the role of nutrition in disease management.

## Contribution

A novel machine learning model is developed to predict CP/CPPS symptom severity using dietary patterns and nutritional data.

## Key findings

- PCA identified two dietary patterns: 'Red Meat and Processed Food' and 'Dairy-rich'.
- XGBoost model achieved the best performance with an AUC of 0.883 in predicting symptom severity.
- SHAP analysis showed the 'Red Meat and Processed Food' pattern was most strongly associated with symptom severity.

## Abstract

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.

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 performance was evaluated using ROC curves, decision curve analysis (DCA), and calibration plots. SHapley Additive exPlanations (SHAP) were used to interpret the optimal model.

PCA identified two major dietary patterns: a “Red Meat and Processed Food” dietary pattern (PC1) and a “Dairy-rich” pattern (PC2). LASSO regression selected key predictors, among which the “Red Meat and Processed Food” dietary pattern demonstrated the strongest positive association with CP/CPPS symptom severity. Among the models, while support vector machine (SVM) and logistic regression showed high AUC values, the XGBoost model demonstrated the best overall performance across a balance of metrics including accuracy, precision, recall, and F1-score, and was selected as the final model (AUC = 0.883). SHAP analysis identified the Red Meat and Processed Food dietary pattern as the most important feature associated with symptom severity.

This study successfully developed a machine learning model based on dietary patterns that effectively predicts CP/CPPS symptom severity. The model underscores the significant association between nutrition and disease management and, with its strong predictive performance and interpretability, offers a novel tool for precision nutrition in CP/CPPS.