Yazar "Atalan, Abdulkadir" seçeneğine göre listele

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    Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms
    (Mdpi, 2024) Atalan, Abdulkadir; Donmez, Cem Cagri
    Hospitals 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.
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    Exploring ChatGPT's role in healthcare management: Opportunities, ethical considerations, and future directions
    (Routledge Journals, Taylor & Francis Ltd, 2024) Atalan, Abdulkadir; Keskin, Abdulkadir; Ozer, Sueleyman
    The 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.
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    Forecasting of the Dental Workforce with Machine Learning Models
    (2024) Atalan, Abdulkadir; Şahin, Hasan
    The 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.
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    Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy
    (Mdpi, 2023) Atalan, Yasemin Ayaz; Atalan, Abdulkadir
    The 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.
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    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, Abdulkadir
    This 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.
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    Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
    (Mdpi, 2025) Atalan, Yasemin Ayaz; Atalan, Abdulkadir
    This 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.