Speaker
Description
Background: Human Immunodeficiency Virus (HIV) continues to be a major global public health challenge, affecting 39.9 million people globally by the end of 2023. Sub-Saharan Africa bears a significant burden, contributing to 67% of cases. Malnutrition is prevalent among people living with HIV, exacerbating immunosuppression and accelerating disease progression.
Objective: This study explored the application of machine learning (ML) to assess the nutritional status of PLWHIV and predict the risk of malnutrition.
Methods: A quantitative cross-sectional study design was employed. Data were collected from the University of Gondar Comprehensive and Specialized Hospital in Ethiopia. The study population included PLWHIV who attended ART clinics. The variables included demographic, clinical, hematological, immunological, and treatment-related factors of the patients. Data preprocessing involves imputation, encoding, and dimensionality reduction. The ML models were trained using an 80:20 train-test split and evaluated in terms of accuracy, precision, recall, F1 score, and AUC.
Results: The study included data from 4,152 respondents, with the majority aged 48-57 years (32.9%), female (59.5%), and living in urban areas (76.5%). Nutritional status assessment revealed that 62.8% of the participants had a normal Body Mass Index, 17.6% were overweight, 15.2% were underweight, and 4.4% were obese.
Machine learning models were evaluated for their ability to predict the risk of malnutrition in PLWHIV. The results showed that applying the Synthetic Minority Oversampling Technique (SMOTE) markedly enhanced model performance by improving minority class recall. A support vector machine (SVM) achieved the highest performance, with an accuracy of 80.1%, precision of 80.4%, recall of 80.1%, F1 score of 79.4%, and an AUC of 0.92. Key predictors of nutritional status included antiretroviral therapy duration, BMI, adherence to treatment, and World Health Organization stage. The integration of the SVM model into electronic medical records could enable real-time malnutrition risk alerts during clinic visits, requiring minimal clinician training.
Conclusion: ML models offer a robust approach for predicting malnutrition risk in PLWHIV patients. The integration of these tools into routine care could enhance nutritional management, particularly in low-resource settings. Further studies are needed to confirm these findings and improve the deployment of the model in clinical settings.
