Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

Abstract

Aims Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection. Methods The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The SMOTE algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction. Results The CatBoost classifier achieved AUC values of 0.7891, 0.6558, 0.6607, 0.6118 and 0.6932 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males.The CatBoost classifier achieved AUC values of 0.7082, 0.647, 0.6767, 0.8459, 0.6929 and 0.7005 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females.