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Öğe Changing the Patient Flow Process for the Green Area of Emergency Department: A Discrete-Event Simulation Approach(Dilaver TENGİLİMOĞLU, 2024) Atalan, AbdulkadirAim: This study aimed to analyze patient waiting times and hospital stay times by developing a different approach to the current patient flow processes in the green area of a hospital emergency department. Methods: Numerical and statistical results were obtained using a discrete event simulation model for the current and new situation of patient flow processes. Results: The waiting time decreased from 12.91 minutes in the current simulation model to 12.81 minutes in the proposed simulation model. The length of stay time decreased from 54.72 minutes in the current simulation model to 53.90 minutes in the proposed simulation model. In this case, it is seen that the proposed model provides a reduction in the length of stay and patient waiting time. Conclusion: The result shows that the proposed model provides some improvement in both waiting and length of stay time. This study offers a different approach to patient flow, allowing emergency department personnel to use their resources effectively and minimizing the time spent on patients' treatment or waiting periods. Thus, this study concluded that the effective management of recommended patient flow processes increases the capacity of emergency departments to deal with emergencies and accelerates access to emergency medical services.Öğe Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms(Mdpi, 2024) Atalan, Abdulkadir; Donmez, Cem CagriHospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.Öğe Exploring ChatGPT's role in healthcare management: Opportunities, ethical considerations, and future directions(Routledge Journals, Taylor & Francis Ltd, 2024) Atalan, Abdulkadir; Keskin, Abdulkadir; Ozer, SueleymanThe ChatGPT initiative, an advanced AI application, has gained significant attention in diverse sectors, including business, technology, and research. Despite its novelty, it has been accepted across various domains, sparking discussions and shaping roadmaps. This article reviews different perspectives on ChatGPT, highlighting existing literature's prevailing lack of standardized evaluation methodologies. The study examines ChatGPT among natural language processing models, exploring its merits, drawbacks, limitations, and future expectations for its application in health quality and management. A meticulous analysis of 104 publications, covering titles, abstracts, and keywords, was independently conducted, addressing four main and seven sub-subjects. The review utilized PubMed, Scopus, Google Scholar, ScienceDirect, and preprint databases such as medRxiv, arXiv, and SSRN to select relevant articles. ChatGPT is expected to efficiently process and analyze extensive healthcare data, facilitating data-driven decision-making for healthcare organizations to enhance their services. However, it is crucial to acknowledge that ChatGPT models are incomplete substitutes for human healthcare professionals and have limitations in addressing complex medical issues. Consequently, this study underscores the imperative for full human supervision in medical services, emphasizing the essential role of humans in ensuring the quality and effective management of healthcare services.Öğe Forecasting of the Dental Workforce with Machine Learning Models(2024) Atalan, Abdulkadir; Şahin, HasanThe aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.Öğe Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy(Mdpi, 2023) Atalan, Yasemin Ayaz; Atalan, AbdulkadirThe importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system's forecast data are under control for the future.Öğe PRICE ESTIMATION OF SELECTED GRAINS PRODUCTS BASED ON MACHINE LEARNING FOR AGRICULTURAL ECONOMIC DEVELOPMENT IN TÜRKİYE(Pakistan Agricultural Scientists Forum, 2024) Keskin, Abdulkadir; Ersin, Irfan; Atalan, AbdulkadirThis study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in T & uuml;rkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R2 values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector.Öğe Process Capability Analysis of Prediction Data of ML Algorithms(İrfan ERSİN, 2024) Altuntaş, Tuğçe; Atalan, AbdulkadirThis study integrates process capability analysis with Machine Learning (ML) methods to optimize business processes. ML, especially Random Forest (RF) and k-nearest neighbor (kNN) algorithms, has enabled the practical analysis of large data sets by using them together with process capability analysis. This integration enabled real-time monitoring and predictive analytics, enabling the proactive identification of process variations and the making of timely adjustments to maintain or increase process capability. Additionally, ML algorithms have helped optimize process parameters and identify critical factors affecting process performance, allowing for continuous improvement and achieving desired quality standards with greater efficiency. In conclusion, this study provides the basis for the synergy between process capability analysis and ML methods to enable businesses to achieve higher levels of quality control, productivity, and competitiveness in dynamic and complex production environmentsÖğe Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance(Mdpi, 2025) Atalan, Yasemin Ayaz; Atalan, AbdulkadirThis study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R-2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R-2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.
