ArticleHealth science reports2023
Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
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- Predicting depression risk in COPD patients: a model based on NHANES 2007-2012 data.BMC public health · 2025Article
- An in-depth study of indolone derivatives as potential lung cancer treatment.Scientific reports · 2025Article
- Identify the underlying true model from other models for clinical practice using model performance measures.BMC medical research methodology · 2025Article
- Enhancing survival prediction for COVID-19 in diabetic patients in Mexico: integrating RMST, propensity score matching, and ensemble machine learning.Frontiers in endocrinology · 2025Article
- Prediction of sporulating Firmicutes from uncultured gut microbiota using SpoMAG, an ensemble learning tool.PeerJ · 2025Article
- Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005-2018.Lipids in health and disease · 2024Article
- Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model.Journal of imaging · 2024Article
- Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit.Journal of clinical laboratory analysis · 2024Article
- The Clínica Universidad de Navarra-Body Adiposity Estimator index is a reliable tool for screening metabolic dysfunction-associated steatotic liver disease: an analysis from a gender perspective.Lipids in health and disease · 2024Article
- Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.Lipids in health and disease · 2024Article
- Severity and mortality of acute respiratory failure in pediatrics: A prospective multicenter cohort in Bogotá, Colombia.Health science reports · 2024Article
- Modeling health risks using neural network ensembles.PloS one · 2024Article
- Article
- Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data.Frontiers in nutrition · 2024Article
- Use machine learning models to identify and assess risk factors for coronary artery disease.PloS one · 2024Article
- Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2023Article
- Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020.Obesity science & practice · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Background and Aims: All fields have seen an increase in machine-learning techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to conduct a critical evaluation of the utilized model metrics, such as sensitivity, specificity, and area under the receiver operator characteristic curve (AUROC). For commonly used model metrics, we proposed the use of analytically derived distributions (ADDs) and compared it with simulation-based approaches. Methods: A retrospective cohort study was conducted using the England National Health Services Heart Disease Prediction Cohort. Four machine learning models (XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boost) were used. The distribution of the model metrics and covariate gain statistics were empirically derived using boot-strap simulation ( Results: XGBoost had the most optimal model having the highest AUROC and the highest aggregate score considering six other model metrics. Based on the Anderson-Darling test, the distribution of the model metrics created from bootstrap did not significantly deviate from a normal distribution. The variance created from the ADD led to smaller SDs than those derived from bootstrap simulation, whereas the rest of the distribution remained not statistically significantly different. Conclusions: ADD allows for cross study comparison of model metrics, which is usually done with bootstrapping that rely on simulations, which cannot be replicated by the reader.
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Registered trials
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.