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Öğe AI-Driven Triage: A Graph Neural Network Approach for Prehospital Emergency Triage Patients in IoMT-Based Telemedicine Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Jasim, Abdulrahman Ahmed; Ata, Oguz; Salman, Omar HusseinImproper triage, overcrowding in the emergency department (ED), and long wait times for patients are some of the problems that arise from emergency triage due to a lack of medical and human resources. In emergency departments, proper patient triage is crucial for determining the urgency and appropriateness of treatment by evaluating the severity of each patient's condition. In most cases, a human operator will use the knowledge and data gathered from patient management to perform triage tactics. Hence, it is a method with the possibility of causing problems in high-priority interconnections. Currently, typical triage systems primarily rely on human recommendations, which are susceptible to personal convictions and error. Methods that maximize data collection and remove mistakes in patient triage processing are being developed with an increasing amount of interest in utilizing artificial intelligence (AI). We develop and deploy an AI module to control the distribution of emergency triage patients in EDs. We use data from past emergency room visits to educate the medical decision-making process. Patients are properly sorted into triage groups because of data including vital signs, symptoms, and an ECG. Test results have shown that the proposed algorithm is better than the currently used state-of-the-art triage techniques. In our opinion, the methods used will help healthcare professionals forecast the severity index, which in turn will help them direct the proper patient management protocols and allocate resources. © 2024 IEEE.Öğe Automated Evaluation of the Virtual Assistant in Bleu and Rouge Scores(Institute of Electrical and Electronics Engineers Inc., 2021) Chatoui, Hajar; Ata, OguzThe advanced field of natural language processing refers to create an intelligent conversational agent. The main goal is to develop bot are capable of generating responses over the last measurements collected over the IoT network. The virtual assistant classifies the user utterance and extract the valuable information to predict the optimal response. The contribution of this paper was performed by the automatics metrics BLEU and ROUGE used in machine translation. However, these metrics were implemented in NLG system to perform the effectiveness of the system. In this work, we evaluated our model over others containing a large set of data in several languages along with the Paradise survey prompts the user satisfaction the results provide F1-score is 74%, the Precision is 85% and Accuracy 75%. Although, the BLEU 0.8 and ROUGE 0.7. The bot has a strong capability to understand dialog human judgments over various features structured based on qualitative and quantitative experiments. © 2021 IEEE.Öğe ENHANCING PREDICTIVE PERFORMANCE IN COVID-19 HEALTHCARE DATASETS: A CASE STUDY BASED ON HYPER ADASYN OVER-SAMPLING AND GENETIC FEATURE SELECTION(Taylors Univ Sdn Bhd, 2024) Mohammedqasim, Hayder; Jasim, Abdulrahman Ahmed; Mohammedqasem, Roa'a; Ata, OguzPredictive analytics is paramount in the health industry, where it finds its wide application, in that it helps increase the forecast's accuracy level based on big data. Most of the time, there is a tendency toward the imbalance of the datasets in healthcare. In this study, two COVID-19 datasets from Kaggle were used as a case study of dataset imbalance. In such scenarios of imbalanced datasets like COVID-19, conventional sampling methods like ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) tend to yield only modest accuracy levels. To address another problem like finding the optimal features, this study proposes a novel approach that combines oversampling techniques with genetic feature selection (GFs) using laboratory data. This innovative method aims to construct machine -learning clinical prediction models for the identification of COVID-19 infected patients, leveraging two widely recognized datasets by using hyper ADASYN over -sampling and genetic feature selection, stands out for its unprecedented precision in identifying relevant features crucial for accurate predictions. Unlike the traditional approach, it can solve the class imbalance problem and tune the feature space to bring about a dramatic increase in accuracy, precision, recall, and overall predictive performance by using our hypermodel. Our approach significantly enhanced the performance of the classifier, and the Random Forest (RF) model with n trees classifies accurately to the limit of 99%, with precision 99%, recall 99%, and F1 -score 99% for each of the datasets. Decision Tree (DT) model achieved 92% with all metrics for Dataset I, and 95% with all metrics for Dataset II. Multilayer Perceptron (MLP) achieved 99% with all metrics, respectively, for both datasets. Gradient Boosting (XGB) achieved 97% for all metrics with dataset I and 98% with all metrics for dataset II. These results underscore the efficacy of our proposed method in balancing COVID-19 datasets and enhancing predictive accuracy.Öğe A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems (vol 8, pg 23169, 2020)(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Moreb, Mohammed; Mohammed, Tareq Abed; Bayat, Oguz; Ata, Oguz[No abstract available]
