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Öğe Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology(Routledge Journals, Taylor & Francis Ltd, 2023) Nath, Gopal; Coursey, Austin; Ekong, Joseph; Rastegari, Elham; Sengupta, Saptarshi; Dag, Asli Z.; Delen, DursunPurpose Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant. The present study aims to uncover the temporal effects of factors that are important for brain and central nervous system (BCNS) cancer survival, by proposing a machine learning methodology. Methods Several feature selection, data balancing, and machine learning algorithms (in addition to the sensitivity analysis) were employed to analyze the dynamic (i.e. varying) effects of several feature sets on the survival outputs. Results The results show that Gradient Boosting (GB) along with Logistic Regression (LR) and Artificial Neural Networks (ANN) outperform the other classification algorithms in this study. Furthermore, it has been observed that the importance of several features/variables varies from 1- to 5- and 10-year survival predictions. Conclusion Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can also be utilized to study survival prognostics of other cancer or chronic disease types.Öğe 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, DursunNearly 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.