BMC medical informatics and decision making · 2025
Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.
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Yeneakal Kelemua Aschale, Teferi Gizaw Hailiye, Mihret Temesgen T, Mengistu Abraham Keffale, Tizie Sefefe Birhanu, Tadele Maru Meseret
Аннотация
BACKGROUND: Adherence with Anti-Retroviral Therapy (ART) reduces viral load, as well as HIV-related morbidity and mortality. Despite the expanded availability of ART, non-adherence remains a series problem, leads increased viral load, a decline CD4 cell count, and the development of drug resistance. HIV care is currently showing promise with the use of machine learning algorithms for early prediction of future non-adherence. However, as to researcher's Knowledge, there was limited research supporting this evidence in the country. Therefore, the primary aim of this study was to predict ART adherence status using machine learning models and to identify the most important predictors of Adherence at Debre Markos comprehensive specialized hospital.
METHODS: Secondary data was collected from ART database of Debre Markos comprehensive specialized hospital, spanning from 2005 to 2024. The dataset was split into training (80%) and testing (20%) sets. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. Seven machine learning algorithms: support vector machine, random forest, decision tree, logistic regression, gradient boosting, K-nearest neighbors, and artificial neural network were trained. The model performance was evaluated using ROC-AUC, F1 score, accuracy, precision, and recall. To identify important predictor we employed feature importance technique.
RESULT: Out of 4640 patients, who were on antiretroviral therapy, 63.56% (n = 2949) were females, with mean age of 41.8 years (SD ± 11.50). The majority age group was between 40 and 59 years (n = 2152) 46.38% and 98.1% of patients had good adherence while 1.9% had poor adherence. Among the machine learning models tested, the gradient boosting algorithm performed better than all other algorithms with (Accuracy = 0.78, Sensitivity = 0.76, F1score = 0.78, AUC = 0.76). Age, regimen, WHO clinical stage, nutritional status, address status, sex, weight, recent CD4 cell count, viral load and ART dose per day were identified as the most important predictors for adherence status.
CONCLUSION: The study developed a gradient boosting model for predicting adherence status. Age, regimen, WHO clinical stage, nutritional status, address status, sex, weight, recent CD4 cell count, viral load and ART dose per day were the most important predictors for adherence status.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: Secondary data was collected from ART database of Debre Markos comprehensive specialized hospital, spanning from 2005 to 2024. The dataset was split into training (80%) and testing (20%) sets. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. Seven machine learning algorithms: support vector machine, random forest, decision tree, logistic regression, gradient boosting, K-nearest neighbors, and artificial neural network were trained. The model performance was evaluated using ROC-AUC, F1 score, accuracy, precision, and recall. To identify important predictor we employed feature importance technique.
RESULT: Out of 4640 patients, who were on antiretroviral therapy, 63.56% (n = 2949) were females, with mean age of 41.8 years (SD ± 11.50). The majority age group was between 40 and 59 years (n = 2152) 46.38% and 98.1% of patients had good adherence while 1.9% had poor adherence. Among the machine learning models tested, the gradient boosting algorithm performed better than all other algorithms with (Accuracy = 0.78, Sensitivity = 0.76, F1score = 0.78, AUC = 0.76). Age, regimen, WHO clinical stage, nutritional status, address status, sex, weight, recent CD4 cell count, viral load and ART dose per day were identified as the most important predictors for adherence status.
CONCLUSION: The study developed a gradient boosting model for predicting adherence status. Age, regimen, WHO clinical stage, nutritional status, address status, sex, weight, recent CD4 cell count, viral load and ART dose per day were the most important predictors for adherence status.
CLINICAL TRIAL NUMBER: Not applicable.