ArticleJournal of education and health promotion2019
Application of data mining techniques for predicting residents' performance on pre-board examinations: A case study.
Article in Journal of education and health promotion, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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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Who cites it
6 citing papers in PubMed.
- A novel approach for accurate disease prediction: Application to heart and liver diseases.Journal of education and health promotion · 2025Article
- Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Using logistic regression to develop a diagnostic model for COVID-19: A single-center study.Journal of education and health promotion · 2022Article
- Explaining Internal Medicine and Surgery Residents' Perceptions of Mentor as a Role Model: A qualitative Study.Medical journal of the Islamic Republic of Iran · 2022Article
- Developing the breast cancer risk prediction system using hybrid machine learning algorithms.Journal of education and health promotion · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextPredicting residents' academic performance is critical for medical educational institutions to plan strategies for improving their achievement.
aimsThis study aimed to predict the performance of residents on preboard examinations based on the results of in-training examinations (ITE) using various educational data mining (DM) techniques. SETTINGS AND
designThis research was a descriptive cross-sectional pilot study conducted at Iran University of Medical Sciences, Iran. PARTICIPANTS AND
methodsA sample of 841 residents in six specialties participating in the ITEs between 2004 and 2014 was selected through convenience sampling. Data were collected from the residency training database using a researcher-made checklist. STATISTICAL ANALYSIS: The analysis of variance was performed to compare mean scores between specialties, and multiple-regression was conducted to examine the relationship between the independent variables (ITEs scores in postgraduate 1
resultsThe ITE scores on the PGY-2 and PGY-3 and the type of specialty training were the predictors of scores on the preboard examination (
conclusionsAccording to the results of the study, MLP-ANN was recognized to be useful in the evaluation of student performance on the ITEs. It is suggested that medical, educational databases be enhanced to benefit from the potential of DM approach in the identification of residents at risk, allowing instructors to offer constructive advice in a timely manner.
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