ArticleGlobal epidemiology2024
Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction.
Article in Global epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Machine learning approaches to identify influential factors associated with hypertension and prehypertension among rural adults in Bangladesh.Journal of public health research · 2026Article
- Physical activity level and its determinants in patients with cancer: a secondary analysis using a decision-tree approach.Journal of cancer survivorship : research and practice · 2026Article
- Comparing the performance of statistical and machine learning survival models in predicting timing and determinants of postnatal care in Rwanda.BMC pregnancy and childbirth · 2026Article
- Application of machine learning algorithm for predicting acute malnutrition among under 5 children in east Africa using recent DHS.Scientific reports · 2026Article
- Classification of anterior cruciate ligament injury profiles through running analysis: a machine learning approach.Scientific reports · 2026Article
- Prediction of myelosuppression risk in postoperative cervical cancer patients undergoing concurrent chemoradiotherapy using machine learning models.Frontiers in oncology · 2026Article
- Neuroimmune Dysregulation and AI-Driven Therapeutic Strategies in Alzheimer's Disease.Cellular and molecular neurobiology · 2025Review
- Development and validation of a machine learning model for predicting venous thromboembolism complications following colorectal cancer surgery.Visual computing for industry, biomedicine, and art · 2025Article
- Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis.BMC public health · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: Research comparing artificial intelligence and machine learning (AI/ML) methods with classical statistical methods applied to large population health databases is limited. Objectives: This retrospective cohort study aimed to compare the predictive performance of AI/ML algorithms against conventional multivariate logistic regression models using linked health administrative data. Methods: Using Ontario's population health databases, we created a cohort of residents of the city of Ottawa, Ontario, who underwent a PCR test for COVID-19 between March 10, 2020, and May 13, 2021. Using demographic, socio-economic and health data (including COVID-19 PCR test results and available, symptom data), we developed predictive models for the purpose of COVID-19 case identification using the following approaches: classical multivariate logistic regression (LR); deep neural network (DNN); random forest (RF); and gradient boosting trees (GBT). Model performance comparisons were made using the area under the curve (AUC) swarm plot for 10-fold cross-validation. Results: The cohort consisted of Conclusions: Conventional multivariate regression-based models are better than some and worse than other machine learning algorithms to provide good predictive accuracy in a moderate dataset with a reasonable number of features. However, whenever possible, the AI/ML GBT approach should be considered.
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