Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study

Abstract

Background HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission. Objective The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. Methods We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external “validation data 1” and data from January 2020 and January 2021 as external “validation data 2.” We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual’s risk of HIV or an STI. Results The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). Conclusions Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

Lightweight federated learning for STIs/HIV prediction

“…The data from the demographic and health surveys (DHS) Program comprised information on behaviour, clinical testing for HIV/STIs (“yes” or “no” for HIV/STIs), and demographic data for men. The variables were selected through an extensive review of relevant literature 8 , 9 , 46 and consultation with experts in public and sexual health. The research project advisory group includes two of the co-authors (A.Prof.…”

“…Regarding these issues, there seems to be a significant emphasis on addressing STIs/HIV prediction, specifically using tabular data as the model input. Recent literature on the application of machine learning to STIs/HIV prediction shows random forest always produces good results compared with other algorithms 7 , 9 , 42 – 44 . The RF algorithm is considered one of the most popular for questionnaire data since it is a non-parametric algorithm and does not apply or assume distribution for data.…”

“…Additionally, RF provided valuable estimates of feature importance, aiding in the identification of the most influential factors for predicting the outcome 5 . In addition, RF worked well with high-dimensional data 6 , and a large number of features, which is important for predicting HIV and STIs, as these conditions are often associated with a variety of risk factors, such as demographic, behavioural, and biological 7 – 9 .…”

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

“…These studies enable individuals to comfortably predict their own risk of HIV and STIs from home. Given that HIV poses higher risks than other STIs, more research has focused on early detection and identi cation of HIV [14][15][16].…”

Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis

“…A study by van de Water et al [12] presented a web-based tool to inform patients about esophagogastric cancer treatment options and their outcomes. These kinds of web applications can also be linked to a trained prediction tool, as demonstrated by Xu et al [13]. They developed a sexually transmitted infection prediction tool.…”