Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

作者：Mengjie Hu, Han Peng, X. Zhang, Lefeng Wang, Jingjing Ren · 发表于：BMC Medical Informatics and Decision Making · 年份：2024 · DOI：10.1186/s12911-024-02426-1 · 被引用次数：13 · 研究领域：Reproductive tract infections research、Cervical Cancer and HPV Research、Adolescent Sexual and Reproductive Health

BACKGROUND AND 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 Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost...