期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：2
DOI：10.14569/IJACSA.2022.0130246
语种：English
出版社：Science and Information Society (SAI)
摘要：Human Immunodeficiency Virus Acquired Immunodeficiency Syndrome (HIV AIDS) in Tanzania is still a threatening disease in society. There have been various strategies to increase the number of people to know their HIV status. Among these strategies, HIV index testing has proven to be the best modality for collecting the number of HIV contacts who might be at risk of contracting HIV from an HIV-positive person. However, the current HIV index testing is manual-based, creating many challenges, including errors, time-consuming, and expensive to operate. Therefore, this paper presents the Machine Learning model results to predict and visualise HIV index testing. The development process followed the Agile Software development methodology. The data was collected from Kilimanjaro, Arusha and Manyara regions in Tanzania. A total of 6346 samples and 11 features were collected. Then, the dataset was divided into training sets of 5075 samples and a testing set of 1270 samples (80/20). The datasets were run into Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) algorithms. The results of the evaluation, by Mean Absolute Errors (MAE), showed that; RF MAE (1.1261), XGBoost MAE (1.2340), and ANN MAE (1.1268.); whereby the RF appeared to have the best result compared to the other two algorithms. Data visualisation shows that 17.4% of males and 82.6 of females had been notified. In addition, the Kilimanjaro region had more cases of people with HIV status from their partners. Overall, this study improved our understanding of the significance of ML in the prediction and visualisation of HIV index testing. The developed model can assist decision-makers in coming out with a suitable intervention strategy towards ending HIV AIDS in our societies. The study recommends that health centres in other regions use this model to simplify their work.
关键词：Index testing; machine learning; random forest; xgboost; artificial neural network
