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Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms

Author

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  • Alejandro Aybar-Flores

    (Department of Engineering, Universidad del Pacífico, Lima 15072, Peru)

  • Alvaro Talavera

    (Department of Engineering, Universidad del Pacífico, Lima 15072, Peru)

  • Elizabeth Espinoza-Portilla

    (Faculty of Health Sciences, School of Medicine, Universidad Continental, Lima 15046, Peru)

Abstract

Inadequate knowledge is one of the principal obstacles for preventing HIV/AIDS spread. Worldwide, it is reported that adolescents and young people have a higher vulnerability of being infected. Thus, the need to understand youths’ knowledge towards HIV/AIDS becomes crucial. This study aimed to identify the determinants and develop a predictive model to estimate HIV/AIDS knowledge among this target population in Peru. Data from the 2019 DHS Survey were used. The software RStudio and RapidMiner were used for quasi-binomial logistic regression and computational model building, respectively. Five classification algorithms were considered for model development and their performance was assessed using accuracy, sensitivity, specificity, FPR, FNR, Cohen’s kappa, F1 score and AUC. The results revealed an association between 14 socio-demographic, economic and health factors and HIV/AIDS knowledge. The accuracy levels were estimated between 59.47 and 64.30%, with the random forest model showing the best performance (64.30%). Additionally, the best classifier showed that the gender of the respondent, area of residence, wealth index, region of residence, interviewee’s age, highest educational level, ethnic self-perception, having heard about HIV/AIDS in the past, the performance of an HIV/AIDS screening test and mass media access have a major influence on HIV/AIDS knowledge prediction. The results suggest the usefulness of the associations found and the random forest model as a predictor of knowledge of HIV/AIDS and may aid policy makers to guide and reinforce the planning and implementation of healthcare strategies.

Suggested Citation

  • Alejandro Aybar-Flores & Alvaro Talavera & Elizabeth Espinoza-Portilla, 2023. "Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms," IJERPH, MDPI, vol. 20(7), pages 1-29, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:7:p:5318-:d:1111212
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    References listed on IDEAS

    as
    1. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    2. Dandan Tang & Man Zhang & Jiabo Xu & Xueliang Zhang & Fang Yang & Huling Li & Li Feng & Kai Wang & Yujian Zheng, 2018. "Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015," Complexity, Hindawi, vol. 2018, pages 1-17, December.
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