[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117436-en":3,"doc-seo-117436-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117436,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Machine Learning Model for Early Detection of Sexually Transmitted Infections","Sexually transmitted infections (STIs) spread mainly through unprotected sex and can also pass from an infected person to an infant before or during childbirth. Worldwide, more than one million STIs are acquired daily, and untreated infections can cause infertility, sterility, increased HIV susceptibility, and even death. Stigma and shame also block timely diagnosis and care. The paper introduces a machine-learning model using 13,335 GoT-HoMIS records and evaluates five algorithms, identifying AdaBoost as best.","Article  \nA machine learning model for early detection of sexually transmitted infections  \nJuma Shija, Judith Leo, Elizabeth Mkoba  \nThe School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania  \nE-mail: [shijaj@nm-aist.ac.tz](shijaj@nm-aist.ac.tz), [judith.leo@nm-aist.ac.tz](judith.leo@nm-aist.ac.tz), [elizabeth.mkoba@nm-aist.ac.tz](elizabeth.mkoba@nm-aist.ac.tz)  \nReceived 18 September 2024; Accepted 25 October 2024; Published online 25 November 2024; Published 1 June 2025  \nAbstract  \nSexually 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  \nlearning-based solutions in mobile apps and telemedicine systems promote early testing and treatment. Keywords machine learning; sexually transmitted infections; artificial intelligence; stigmatisation.  \nComputational Ecology and Software  \nISSN 2220 721X  \nURL: [http://www.iaees.org/publications/journals/ces/online version.asp](http://www.iaees.org/publications/journals/ces/online version.asp)  \nRSS: [http://www.iaees.org/publications/journals/ces/rss.xml](http://www.iaees.org/publications/journals/ces/rss.xml)  \nE mail: [ces@iaees.org](ces@iaees.org)  \nEditor in Chief: WenJun Zhang  \nPublisher: International Academy of Ecology and Environmental Sciences  \nIAEES [www.iaees.org](www.iaees.org)  \n1 Introduction  \nAccording to the World Health Organization (WHO), more than one million sexually transmitted infections (STI) are acquired every day worldwide. Each year, there are an estimated 376 million new infections with 1 of 4 STIs such as chlamydia, gonorrhoea, syphilis, and trichomoniasis (WHO, 2019) . According to the literature, it is believed that the same trend or at a higher rate is happening in Tanzania; however, it is difficult to track the data because most patients feel ash","cbCaid3K5GlISeUc","https://ap.wps.com/l/cbCaid3K5GlISeUc","pdf",331172,1,15,"English","en",105,"# Introduction\n## Problem background and epidemiology\n## Stigma and data-collection challenges\n## Need for automated machine learning systems","[{\"question\":\"What problem does the paper address for sexually transmitted infections (STIs)?\",\"answer\":\"The paper targets early detection of STIs while addressing barriers such as stigma that make patients delay diagnosis and reduce reporting quality.\"},{\"question\":\"What dataset and methodology were used to build the model?\",\"answer\":\"The study used quantitative research with 13,335 records from Tanzania’s GoT-HoMIS, collected via surveys and questionnaires, then split the data into 70% training, 15% testing, and 15% validation.\"},{\"question\":\"Which machine learning algorithm performed best, and what were the key results?\",\"answer\":\"AdaBoost showed the strongest performance, reaching 97.45% accuracy and 97.7% F1 score, with ROC-AUC indicating a higher true positive rate and lower false positive rate.\"}]","A Machine Learning Model for Early Detection of Sexually Transmitted Infections | 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problem does the paper address for sexually transmitted infections (STIs)?","Question",{"text":75,"@type":76},"The paper targets early detection of STIs while addressing barriers such as stigma that make patients delay diagnosis and reduce reporting quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and methodology were used to build the model?",{"text":80,"@type":76},"The study used quantitative research with 13,335 records from Tanzania’s GoT-HoMIS, collected via surveys and questionnaires, then split the data into 70% training, 15% testing, and 15% validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithm performed best, and what were the key results?",{"text":84,"@type":76},"AdaBoost showed the strongest performance, reaching 97.45% accuracy and 97.7% F1 score, with ROC-AUC indicating a higher true positive rate and lower false positive 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