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All Journal International Journal of Electrical and Computer Engineering Journal of Electronics, Electromedical Engineering, and Medical Informatics
Billah, Mohammed El Kaim
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency departments Abatal, Ahmed; Mzili, Mourad; Benlalia, Zakaria; Khallouki, Hajar; Mzili, Toufik; Billah, Mohammed El Kaim; Abualigah, Laith
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp669-676

Abstract

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.
A Comprehensive Evaluation of Machine Learning Techniques for Forecasting Student Academic Success Abatal, Ahmed; Korchi, Adil; Mzili, Mourad; Mzili, Toufik; Khalouki, Hajar; Billah, Mohammed El Kaim
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.489

Abstract

Improving academic outcomes relies on accurately anticipating student outcomes within a course or program. This predictive capability empowers instructional leaders to optimize the allocation of resources and tailor instruction to meet individual student needs more effectively. In this study, we endeavor to delineate the attributes of machine learning algorithms that excel in forecasting student grades. Leveraging a comprehensive dataset encompassing both personal student information and corresponding grades, we embark on a rigorous evaluation of various regression algorithms. Our analysis encompasses a range of widely used technniques, Incorporating various machine learning algorithms like XGBoost, Linear Regression, K-Nearest Neighbor, Decision Tree, Random Forest, and Deep Neural Network. By conducting thorough comparisons using metrics such as Root Mean Squared Error, determination coefficient, Mean Average Error and Mean Squared Error. Our aim is to pinpoint the algorithm that exhibits superior predictive ability. Notably, our experimental findings unveil the deep neural network as the standout performer among the evaluated algorithms. Having an outstanding coefficient of determination of 99.95% and Minimal error margins, the DNN emerges as a potent tool for accurately forecasting student grades. This discovery not only underscores the efficacy of advanced machine learning methodologies but also underscores the transformative potential they hold in shaping educational practices and optimizing student outcomes.
Co-Authors Abatal, Ahmed Abualigah, Laith Benlalia, Zakaria Khallouki, Hajar Khalouki, Hajar Korchi, Adil Mzili, Mourad Mzili, Toufik