Journal of Education and Health Promotion

Predicting intubation risk among COVID‑19 hospitalized patients using artificial neural networks

. Raoof Nopour; . Mostafa Shanbezadeh; . Hadi Kazemi-Arpanahi

Volume 13, Issue 1 , January 2023, , Pages 1-8

Abstract

BACKGROUND: Accurately predicting the intubation risk in COVID‑19 patients at the admissiontime is critical to optimal use of limited hospital resources, providing customized and ... Read More

Performance analysis of data mining algorithms for diagnosing COVID‑19

. Raoof Nopour; . Hadi Kazemi‑Arpanahi; . Mostafa Shanbehzadeh; . Akbar Azizifar

Volume 11, Issue 10 , November 2021, , Pages 1-8

Abstract

BACKGROUND: An outbreak of atypical pneumonia termed COVID‑19 has widely spread all overthe world since the beginning of 2020. In this regard, designing a prediction system for the ... Read More

Performance evaluation of selected machine learning algorithms for COVID‑19 prediction using routine clinical data: With versus Without CT scan features

. Mostafa Shanbehzadeh; . Hadi Kazemi-Arpanahi; . Azam Orooji; . Sara Mobarak; . Saeed Jelvay

Volume 11, Issue 7 , August 2021, , Pages 1-11

Abstract

BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatmentis very complex up to the present time. Thus, it is essential to have a framework ... Read More

Application of data mining techniques for predicting residents’ performance on pre‑board examinations: A case study

. Leila Amirhajlou; . Zohre Sohrabi; . Mahmoud Reza Alebouyeh; . Nader Tavakoli; . Roghye Zare Haghighi; . Akram Hashemi; . Amir Asoodeh

Volume 9, Issue 6 , June 2019, , Pages 1-7

Abstract

CONTEXT: Predicting residents’ academic performance is critical for medical educational institutionsto plan strategies for improving their achievement.AIMS: This study aimed to ... Read More CONTEXT: Predicting residents’ academic performance is critical for medical educational institutionsto plan strategies for improving their achievement.AIMS: This study aimed to predict the performance of residents on preboard examinations basedon the results of in‑training examinations (ITE) using various educational data mining (DM)techniques.SETTINGS AND DESIGN: This research was a descriptive cross‑sectional pilot study conducted atIran University of Medical Sciences, Iran.PARTICIPANTS AND METHODS: A sample of 841 residents in six specialties participating in theITEs between 2004 and 2014 was selected through convenience sampling. Data were collectedfrom the residency training database using a researcher‑made checklist.STATISTICAL ANALYSIS: The analysis of variance was performed to compare mean scoresbetween specialties, and multiple‑regression was conducted to examine the relationship betweenthe independent variables (ITEs scores in postgraduate 1st year [PGY1] to PG 3rd year [PGY3],sex, and type of specialty training) and the dependent variable (scores of postgraduate 4th yearcalled preboard). Next, three DM algorithms, including multi‑layer perceptron artificial neuralnetwork (MLP‑ANN), support vector machine, and linear regression were utilized to build theprediction models of preboard examination scores. The performance of models was analyzedbased on the root mean square error (RMSE) and mean absolute error (MAE). In the final step,the MLP‑ANN was employed to find the association rules. Data analysis was performed in SPSS22 and RapidMiner 7.1.001.RESULTS: The ITE scores on the PGY‑2 and PGY‑3 and the type of specialty training were thepredictors of scores on the preboard examination (R2 = 0.129, P < 0.01). The algorithm with theoverall best results in terms of measuring error values was MLP‑ANN with the condition of ten‑foldcross‑validation (RMSE = 0.325, MAE = 0.212). Finally, MLP‑ANN was utilized to find the efficientrules.CONCLUSIONS: According to the results of the study, MLP‑ANN was recognized to be useful in theevaluation of student performance on the ITEs. It is suggested that medical, educational databasesbe 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.

A novel AIDS/HIV intelligent medical consulting system based on expert systems

. Alireza Pour Ebrahimi; . Abbas Toloui Ashlaghi; . Maryam Mahdavy Rad

Volume 3, Issue 9 , September 2013, , Pages 1-8

Abstract

Background: The purpose of this paper is to propose a novel intelligent model for AIDS/HIVdata based on expert system and using it for developing an intelligent medical consultingsystem ... Read More

Journal of Education and Health Promotion

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Keywords = data mining

Predicting intubation risk among COVID‑19 hospitalized patients using artificial neural networks

Abstract

Performance analysis of data mining algorithms for diagnosing COVID‑19

Abstract

Performance evaluation of selected machine learning algorithms for COVID‑19 prediction using routine clinical data: With versus Without CT scan features

Abstract

Application of data mining techniques for predicting residents’ performance on pre‑board examinations: A case study

Abstract

A novel AIDS/HIV intelligent medical consulting system based on expert systems

Abstract