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Computational Ecology and Software, 2025, 15(2): 30-44
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Article

A machine learning model for early detection of sexually transmitted infections

Juma Shija, Judith Leo, Elizabeth Mkoba
The School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

Received 18 September 2024;Accepted 25 October 2024;Published online 25 November 2024;Published 1 June 2025
IAEES

Abstract
Sexually transmitted infections (STIs) are diseases transmitted mostly through unprotected sex with an infected partner. STIs can be transmitted to an infant before or during childbirth. More than one million sexually transmitted infections (STIs) are acquired every day worldwide. In the most recent years, the prevalence of STIs reached approximately 20% among Tanzanian older adults living in metropolitan areas. If not treated properly and on time, STIs can have severe consequences, including infertility, sterility, increased susceptibility to more serious diseases such as the Human Immunodeficiency Virus (HIV), and even death. However, stigma and shame associated with STIs remain significant barriers to proper diagnosis and timely treatment, leading many patients to face increased risks. The purpose of this paper is to present a machine-learning model for early detection of sexually transmitted infections that was developed. The developed model can be deployed into health systems for self-diagnosis to remove communication barriers between sexual health clinics and STI patients. The study used a quantitative research method and got its dataset of 13,335 records from the Government of Tanzania Health Operations Management Information System (GoT-HoMIS) in areas with many STI cases. This was done by using surveys and questionnaires to get the data. The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. Based on evaluation metrics, the AdaBoost model was identified as the best-performing model, achieving an accuracy of 97.45%, an F1 score of 97.7%, and the Receiver Operating Characteristics Area Under the Curve (ROC-AUC) with a higher true positive rate and a lower false positive rate. The study recommends integrating a machine learning model into healthcare systems to detect STIs early, improve medical care, reduce disease progression, and remove stigmatisation barriers. Also, it can provide insights into infection patterns, allowing practitioners to adapt their responses. Machine learning-based solutions in mobile apps and telemedicine systems promote early testing and treatment.

Keywords machine learning;sexually transmitted infections;artificial intelligence;stigmatisation.


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