HIV/AIDS predictive model using random forest based on socio-demographical, biological and behavioral data

Nisa, Sehar Un; Mahmood, Azhar; Ujager, Farhan Sabir; Malik, Mehwish

doi:10.1016/j.eij.2022.12.005

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…This will make a great contribution to the global target of ending HIV as a public health threat. Prediction models direct the decision-making process in the choice of interventions through the estimation of individualized risk with the aim of achieving better outcomes ( 30 , 31 , 41 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation

Tesfie,

Yehuala,

Agimas

et al. 2024

Front. Public Health

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IntroductionWomen are more vulnerable to HIV infection due to biological and socioeconomic reasons. Developing a predictive model for these vulnerable populations to estimate individualized risk for HIV infection is relevant for targeted preventive interventions. The objective of the study was to develop and validate a risk prediction model that allows easy estimations of HIV infection risk among sexually active women in Ethiopia.MethodsData from the 2016 Ethiopian Demographic and Health Survey, which comprised 10,253 representative sexually active women, were used for model development. Variables were selected using the least absolute shrinkage and selection operator (LASSO). Variables selected by LASSO were incorporated into the multivariable mixed-effect logistic regression model. Based on the multivariable model, an easy-to-use nomogram was developed to facilitate its applicability. The performance of the nomogram was evaluated using discrimination and calibration abilities, Brier score, sensitivity, and specificity. Internal validation was carried out using the bootstrapping method.ResultsThe model selected seven predictors of HIV infection, namely, age, education, marital status, sex of the household head, age at first sex, multiple sexual partners during their lifetime, and residence. The nomogram had a discriminatory power of 89.7% (95% CI: 88.0, 91.5) and a calibration p-value of 0.536. In addition, the sensitivity and specificity of the nomogram were 74.1% (95% CI: 68.4, 79.2) and 80.9% (95% CI: 80.2, 81.7), respectively. The internally validated model had a discriminatory ability of 89.4% (95% CI: 87.7, 91.1) and a calibration p-value of 0.195. Sensitivity and specificity after validation were 72.9% (95% CI: 67.2, 78.2) and 80.1% (95% CI: 79.3, 80.9), respectively.ConclusionA new prediction model that quantifies the individualized risk of HIV infection has been developed in the form of a nomogram and internally validated. It has very good discriminatory power and good calibration ability. This model can facilitate the identification of sexually active women at high risk of HIV infection for targeted preventive measures.

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Section: Discussionmentioning

confidence: 99%

Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation

Tesfie,

Yehuala,

Agimas

et al. 2024

Front. Public Health

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“…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.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

Lightweight federated learning for STIs/HIV prediction

Nguyen,

Yang,

Tang

et al. 2024

Sci Rep

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This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ($$93\%$$ 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation.

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“…The application of AI in the health sector especially in early detection and diagnosis of HIV has proven to be effective in the management and early detection of HIV [17]. Nisa et al [18] developed a predictive system that considers other factors in a high-risk group while predicting the future acquisition of HIV. Such attributes are drug injection, sexual behavior, and other behavioral and biological factors.…”

Section: Early Detection and Diagnosismentioning

confidence: 99%

Personalized risk reduction of hiv plans with artificial intelligence: a narrative review

C.U,

J.A.,

O.I

et al. 2024

KJHS

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This narrative review explores the current landscape and future potential of utilizing Artificial Intelligence (AI) in the development and implementation of personalized risk reduction plans for individuals at risk of HIV infection. Traditional HIV prevention strategies often adopt a generic approach, overlooking the diverse and dynamic factors contributing to an individual's risk profile. In contrast, this review synthesizes existing literature to highlight recent advancements in AI applications, focusing on their role in tailoring HIV risk reduction interventions to the unique characteristics and circumstances of each individual. The review encompasses studies employing machine learning algorithms, predictive modeling, and data analytics to analyze and interpret large datasets related to HIV epidemiology, behavioral patterns, and socio-economic determinants. By providing an overview of these AI-driven methodologies, the review aims to showcase the potential for personalized risk assessment and intervention planning. Furthermore, it examines the integration of AI into mobile health applications, wearable devices, and telehealth platforms, facilitating real-time monitoring, feedback, and support for individuals seeking personalized risk reduction strategies.

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HIV/AIDS predictive model using random forest based on socio-demographical, biological and behavioral data

Cited by 10 publications

References 15 publications

Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation

Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation

Lightweight federated learning for STIs/HIV prediction

Personalized risk reduction of hiv plans with artificial intelligence: a narrative review

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