ArticleMultimedia tools and applications2022
A diagnostic testing for people with appendicitis using machine learning techniques.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 53 citations in OpenAlex.
- Can Artificial Intelligence Revolutionise Surgical Decision-Making for Appendectomy? A Narrative Review.Surgical innovation · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- 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
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model.World journal of clinical cases · 2025Article
- Review
- An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients.Scientific reports · 2024Article
- Fully Automated Detection of the Appendix Using U-Net Deep Learning Architecture in CT Scans.Journal of clinical medicine · 2024Article
- Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings.Scientific reports · 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
- Use of machine learning models for the prognostication of liver transplantation: A systematic review.World journal of transplantation · 2024Article
- Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis.Scientific reports · 2023Article
- Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients.World journal of orthopedics · 2023Article
- Revolutionizing healthcare: the role of artificial intelligence in clinical practice.BMC medical education · 2023Review
- 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
- Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging.World journal of radiology · 2023Review
- Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.
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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.