ArticleDiagnostic and prognostic research2020
Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults.
Article in Diagnostic and prognostic research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 4 of them syntheses that pooled it.
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Who cites it
61 citing papers in PubMed, 4 syntheses or guidelines pooled it, 132 citations in OpenAlex.
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- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- Estimating postoperative mortality in colorectal surgery- a systematic review of risk prediction models.International journal of colorectal disease · 2023Pooled it
- Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.The Cochrane database of systematic reviews · 2021Pooled it
- Dose-conditioned machine-learning prediction of a composite target sedation state in patients undergoing propofol-sedated gastrointestinal endoscopy.Annals of medicine · 2026Observational
- Risk factor analysis and predictive model construction and evaluation of fractional uric acid excretion in Chinese inpatients with type 2 diabetes.Scientific reports · 2026Article
- Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.Annals of surgical oncology · 2026Article
- Machine Learning-Based Prediction of Masaoka-Koga Stage and WHO Histological Risk Group in Thymic Epithelial Tumors Using Biomarker Combinations.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning models for predicting metabolic syndrome to support clinical decision-making in ART-treated adults living with HIV.BMC medical informatics and decision making · 2026Article
- Development and Internal Validation of a Machine Learning Model for Predicting Long-Term Opioid Therapy After Hip Fracture Surgery in Older, Opioid-Naïve Adults.European journal of pain (London, England) · 2026Article
- Prediction of low 5-minute Apgar scores: development and internal validation of parity-stratified clinical prediction models for sub-Saharan Africa.BMC pregnancy and childbirth · 2026Article
- Type 2 diabetes care: knowledge and clinical practice among medical practitioners at Medical Teaching Institute Mardan, Pakistan - a cross-sectional study.BMC primary care · 2026Article
- From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts.Cureus · 2025Review
- Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.Communications medicine · 2025Article
- A microRNA-based dynamic risk score for type 1 diabetes.Nature medicine · 2025Article
- IntegratingCancers · 2025Article
- Statistical models versus machine learning approach for competing risks in proctological surgery.Updates in surgery · 2025Article
- Advancing Pediatric Growth Assessment with Machine Learning: Overcoming Challenges in Early Diagnosis and Monitoring.Children (Basel, Switzerland) · 2025Article
- Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis.Intensive care medicine experimental · 2025Review
- Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThere is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models.
methodsWe trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (
resultsAverage performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities.
conclusionLogistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.