Yazar "Kivrak, Mehmet" seçeneğine göre listele

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    Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set
    (Ieee, 2019) Akbas, Kubra Elif; Kivrak, Mehmet; Arslan, A. Kadir; Colak, Cemil
    Association rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.
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    PREDICTION OF COVID-19 SEVERITY IN SARS-COV-2 RNA-POSITIVE PATIENTS BY DIFFERENT ENSEMBLE LEARNING STRATEGIES
    (Carbone Editore, 2022) Bag, Harika Gozde Gozukara; Kivrak, Mehmet; Guldogan, Emek; Colak, Cemil
    Introduction: While the coronavirus only persists marginally for 95% of the infected cases, the remaining 5% are in critical or life-threatening conditions. This study aimed to design an intelligent model that predicts the severity level of the disease by modeling the relationships between the COVID-19 infection severity and the various demographic/clinical features of individuals. Materials and methods: A public dataset of a cross-sectional study including the demographic and symptomatological characteristics of 223 COVID-19 patients was used and randomly partitioned into training (75%) and testing (25%) datasets. During training, the class imbalance problem was solved, and the related factors with the COVID-19 severity were selected using the evolutionary method supported by a genetic algorithm. Neural Network (NN), Support Vector Machine (SVM), QUEST algorithms together with confidence weighted voting, voting, and highest confidence wins strategies (HCWS) were constructed, and the predictive power of models was determined by performance metrics. Results: Based on the performance indicators, among the individual models, the NN model outperformed SVM and QUEST algorithms in the training and testing datasets. However, ensemble approaches gave better predictions as compared to individual models according to all the evaluation metrics. Conclusion: The proposed voting ensemble model outperforms other ensemble and individual machine learning approaches for the severity prediction of COVID-19 disease. The proposed ensemble learning model can be integrated into web or mobile applications to classify the severity of COVID-19 for clinical decision support.
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    Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods
    (Elsevier Ireland Ltd, 2021) Kivrak, Mehmet; Guldogan, Emek; Colak, Cemil
    Background and Objective: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus, which first appeared as a sign of pneumonia, was later called the SARS-COV-2 with Severe Acute Respiratory Syndrome by the World Health Organization. The SARS-COV-2 virus is triggered by binding to the Angiotensin-Converting Enzyme 2 (ACE 2) inhibitor, which is vital in cardiovascular diseases and the immune system, especially in conditions such as cerebrovascular, hypertension, and diabetes. This study aims to evaluate the prediction performance of death status based on the demographic/clinical factors (including COVID-19 severity) by data mining methods. Methods: The dataset consists of 1603 SARS-COV-2 patients and 13 variables obtained from an open source web address. The current dataset contains age, gender, chronic disease (hypertension, diabetes, renal, cardiovascular, etc.), some enzymes (ACE, angiotensin II receptor blockers), and COVID-19 severity, which are used to predict death status using deep learning and machine learning approaches (random forest, k-nearest neighbor, extreme gradient boosting [XGBoost]). A grid search algorithm tunes hyperparameters of the models, and predictions are assessed through performance metrics. Steps of knowledge discovery in databases are applied to obtain the relevant information. Results: The accuracy rate of deep learning (97.15%) was more successful than the accuracy rate based on classical machine learning (92.15% for RF and 93.4% for k-NN), but the ensemble classifier XGBoost method gave the highest accuracy (99.7%). While COVID-19 severity and age calculated from XGBoost were the two most important factors associated with death status, the most determining variables for death status estimated from deep learning were COVID-19 severity and hypertension. Conclusions: The proposed model (XGBoost) achieved the best prediction of death status based on the factors as compared to the other algorithms. The results of this study can guide patients with certain variables to take early measures and access preventive health care services before they become infected with the virus. (c) 2021 Elsevier B.V. All rights reserved.