ArticleEmergency medicine international2020
The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis.
Article in Emergency medicine international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- Machine learning-based prediction of acute and complicated appendicitis using readily available data in low-resource settings.PloS one · 2026Article
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model.World journal of clinical cases · 2025Article
- Evaluating Ensemble-Based Machine Learning Models for Diagnosing Pediatric Acute Appendicitis: Insights from a Retrospective Observational Study.Journal of clinical medicine · 2025Article
- Review
- Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data.Health science reports · 2025Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- External validation of predictive models for diagnosis, management and severity of pediatric appendicitis.Frontiers in pediatrics · 2025Article
- Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings.Scientific reports · 2024Article
- Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques.Scientific reports · 2024Article
- A method for predicting mortality in acute mesenteric ischemia: Machine learning.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2024Article
- Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians.BMC emergency medicine · 2024Article
- Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis.Scientific reports · 2024Article
- Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review.Updates in surgery · 2024Review
- Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults.Yonsei medical journal · 2024Article
- Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis.Scientific reports · 2023Article
- Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Validity of Machine Learning in Detecting Complicated Appendicitis in a Resource-Limited Setting: Findings from Vietnam.BioMed research international · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.
Identifiers
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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.