Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Guney, SeldaSubject: search.filters.subject.classification

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    Heart Disease Prediction by Using Machine Learning Algorithms
    (2020) Erdogan, Alperen; Guney, Selda
    Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.
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    Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones
    (2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484
    Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.