ArticlePloS one2022
Machine learning outperformed logistic regression classification even with limit sample size: A model to predict pediatric HIV mortality and clinical progression to AIDS.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Predictive model of neurocognitive functioning after acute coronary syndrome. A machine learning approach.Journal of cardiovascular and thoracic research · 2025Article
- Leveraging small-sample machine learning for rigorous prediction of JOA recovery in cervical spondylotic myelopathy patients: insights from imaging parameters and modeling strategies.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Machine learning models for risk prediction of age-related macular degeneration in Fujian eye study.PloS one · 2025Article
- Application of machine learning in breast cancer survival prediction using a multimethod approach.Scientific reports · 2024Article
- Early inflammation as a footprint of increased mortality risk in infants living with HIV from three African countries.Scientific reports · 2024Article
- Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.BMC anesthesiology · 2024Article
- Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases.Frontiers in microbiology · 2024Review
- A Machine Learning-Based Method for Detecting Liver Fibrosis.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial-Intelligence-Based Models Coupled with Correspondence Analysis Visualization on ART-Cases from Gombe State, Nigeria: A Comparative Study.Life (Basel, Switzerland) · 2023Article
- Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.Advances in experimental medicine and biology · 2023Article
- Article
- Interpretable machine learning prediction of in-hospital mortality in people with HIV: A cohort study in southeastern China.Digital healthArticle
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Authors and funding
23 authors.
Funding
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Abstract
Logistic regression (LR) is the most common prediction model in medicine. In recent years, supervised machine learning (ML) methods have gained popularity. However, there are many concerns about ML utility for small sample sizes. In this study, we aim to compare the performance of 7 algorithms in the prediction of 1-year mortality and clinical progression to AIDS in a small cohort of infants living with HIV from South Africa and Mozambique. The data set (n = 100) was randomly split into 70% training and 30% validation set. Seven algorithms (LR, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), and Elastic Net) were compared. The variables included as predictors were the same across the models including sociodemographic, virologic, immunologic, and maternal status features. For each of the models, a parameter tuning was performed to select the best-performing hyperparameters using 5 times repeated 10-fold cross-validation. A confusion-matrix was built to assess their accuracy, sensitivity, and specificity. RF ranked as the best algorithm in terms of accuracy (82,8%), sensitivity (78%), and AUC (0,73). Regarding specificity and sensitivity, RF showed better performance than the other algorithms in the external validation and the highest AUC. LR showed lower performance compared with RF, SVM, or KNN. The outcome of children living with perinatally acquired HIV can be predicted with considerable accuracy using ML algorithms. Better models would benefit less specialized staff in limited resources countries to improve prompt referral in case of high-risk clinical progression.
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