Yazar "Asilkalkan, Abdullah" seçeneğine göre listele

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    A parsimonious tree augmented naive bayes model for exploring colorectal cancer survival factors and their conditional interrelations
    (Springer, 2024) Dağ, Ali; Asilkalkan, Abdullah; Aydaş, Osman T.; Çağlar, Musa; Şimşek, Serhat; Delen, Dursun
    Effective management of colorectal cancer (CRC) necessitates precise prognostication and informed decision-making, yet existing literature often lacks emphasis on parsimonious variable selection and conveying complex interdependencies among factors to medical practitioners. To address this gap, we propose a decision support system integrating Elastic Net (EN) and Simulated Annealing (SA) algorithms for variable selection, followed by Tree Augmented Naive Bayes (TAN) modeling to elucidate conditional relationships. Through k-fold cross-validation, we identify optimal TAN models with varying variable sets and explore interdependency structures. Our approach acknowledges the challenge of conveying intricate relationships among numerous variables to medical practitioners and aims to enhance patient-physician communication. The stage of cancer emerges as a robust predictor, with its significance amplified by the number of metastatic lymph nodes. Moreover, the impact of metastatic lymph nodes on survival prediction varies with the age of diagnosis, with diminished relevance observed in older patients. Age itself emerges as a crucial determinant of survival, yet its effect is modulated by marital status. Leveraging these insights, we develop a web-based tool to facilitate physician-patient communication, mitigate clinical inertia, and enhance decision-making in CRC treatment. This research contributes to a parsimonious model with superior predictive capabilities while uncovering hidden conditional relationships, fostering more meaningful discussions between physicians and patients without compromising patient satisfaction with healthcare provision.
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    Streamlining patients' opioid prescription dosage: an explanatory bayesian model
    (Springer, 2023) Asilkalkan, Abdullah; Dag, Asli Z.; Simsek, Serhat; Aydas, Osman T.; Kibis, Eyyub Y.; Delen, Dursun
    Nearly half a million people died between 1999 and 2019 from overdosing on both prescribed and illicit opioids. Thus, much research has been devoted to determining the factors affecting the dosages of opioid prescriptions. In this study, we build a probabilistic data-driven framework that develops Tree Augmented Naive Bayes (TAN) models to predict patients' opioid prescription dosage categories and investigate the conditional interrelations among these predictors. As this framework is rooted in the CDC's prescription guidelines, it can be applied in clinical settings by focusing primarily on pre-discharge pain assessments. Following data acquisition and cleaning, we utilize Elastic Net (EN) and Genetic Algorithm (GA) to identify the most important predictors. Next, Synthetic Minority Oversampling Technique (SMOTE), and Random Under Sampling (RUS) are employed to overcome the data imbalance problem present in the dataset. A patient's gender, income level, smoking status, BMI, age, and length of stay at the hospital are identified as the most significant predictors for opioid prescription dosage. In addition, we construct a Bayesian Belief Network (BBN) model, which reveals that the effect of smoking status and gender in predicting opioid prescription dosage depends on the patient's income level. Finally, a web-based decision support tool that can help surgeons better assess and prescribe appropriate opioid dosages for patients is built.
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    A tree augmented naive bayes-based methodology for classifying cryptocurrency trends
    (ELSEVIER, 2023) Dağ, Ali; Dağ, Aslı Z.; Asilkalkan, Abdullah; Şimşek, Serhat; Delen, Dursun
    As the popularity of blockchain technology and investor confidence in Bitcoin (BTC) increased in recent years, many individuals started making BTC and other cryptocurrency investments, in expectation of high returns. However, as recent market movements have shown, the lack of regulation and oversight makes it difficult to guard against high volatility and potentially significant losses in this sector. In this study, we propose a datadriven Tree Augmented Naive (TAN) Bayes methodology that can be used for identifying the most important factors (as well as their conditional, interdependent relationships) influencing BTC price movements. As the model is parsimonious without sacrificing accuracy, sensitivity, and specificity-as evident from the average accuracy value-the proposed methodology can be used in practice for making short-term investment decisions.