Browsing by Author "Erkal, Begum"

Now showing 1 - 3 of 3

Multiclass Classification of Brain Cancer with Machine Learning Algorithms
(2020) Erkal, Begum; Basak, Selen; Ciloglu, Alper; Sener, Duygu Dede
Brain cancer is one the most important disease to be treated all around the world. Classification of brain cancer using machine learning techniques has been widely studied by researchers. Microarray gene expression data are commonly used medical data to get observable results in this manner. In this study, multiclass classification of brain cancer is aimed by using different machine learning approaches. Some preprocessing methods were applied to get improved results. According to the result, feature selection has greatly affected the overall performance of each method in terms of overall accuracy and per class accuracy. Experimental results show that Multilayer Perceptron (MP) method has higher accuracy rate compared with other machine learning methods.
Predicting Diabetes Using Machine Learning Techniques
(2022) Kirgil, Elif Nur Haner; Erkal, Begum; Ayyildiz, Tulin Ercelebi; 0000-0002-7372-0223; JBI-6492-2023
Early diagnosis of diabetes, which can cause death, is very important for the health of the person. In the literature, machine learning techniques are frequently used in diagnosis of many diseases, including diabetes. The aim of the study is to predict diabetes with high accuracy by using machine learning and preprocessing techniques. Pima Indian Diabetes dataset was used in the study. J48 (Decision Tree), Naive Bayes, Support Vector Machine, Logistic Regression, Multilayer Perceptron, K Nearest Neighbor, Logistic Model Tree, and Random Forest were used for classification. Of the preprocessing methods, feature selection, imputing missing values, normalization and standardization are performed. According to the results obtained, the highest accuracy value got with the Random Forest algorithm as 80.869.
Using Machine Learning Methods in Early Diagnosis of Breast Cancer
(2021) Erkal, Begum; Ayyildiz, Tulin Ercelebi; https://orcid.org/0000-0002-7372-0223; JBI-6492-2023
Breast cancer is one of the most important health diseases to be treated in the world, and it is a subject that has a wide place in research subjects. In this study, results are provided by using seven different machine learning techniques for the classification of breast cancer. In order to obtain better results, the preprocessing method was applied. As a result, when compared with some studies in the literature, it was observed that the general performance of some of the methods improved. In the experimental results, BayesNet was found to be the best classification method with an accuracy rate of 97.13%.