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Mortality Prediction of Countries
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Üşenmez, Elif Efser; Koç, Utku
In this study mortality reasons of countries detailed by sex and age-group is analyzed and different forecasting models are developed by using different machine learning algorithms. The dataset is obtained from the World Health Organization(WHO) Mortality Database. In WHO database there are different datasets for countries mortality reason number. The study used the dataset that used ICD-10 for classifying mortality reasons.ICD-10 is the 10 revision of International Statistical Classification of Diseases and Related Health Problems published by the World Health Organization. In addition to main mortality reason datasets, we add different independent variables and try to find the best features to fit models without biasing and overfitting and reaching high R2 and Mean Square Errors. To find the best model for forecasting mortality reasons by age-groups and sex different machine learning algorithms are fitted and results of these algorithms are analyzed.
Predicting Birth Defects
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Korkut Özer, Selen; Koç, Utku
Many couples are eager to have a healthy baby. For this reason, the pregnant woman is trying to take their baby through the steps of adjusting their lives during the pregnancy, such as healthy nutrition, organic life, avoiding cosmetics. Even though the woman can do it, health problems can be observed in the baby at the time of birth or after birth. The causes of these health problems may be factors such as genetic, the physiological characteristics of the mother, environmental. In this paper, we tried to answer the question whether the health problems that occur in babies after childbirth can be estimated before birth. This includes the birth records of the American Centers for Disease Control and Prevention (CDC). Approximately 3M data was analyzed and the prediction model worked on the baby dataset. Boosting, Random Forest, Neural Network, Logistic Regression and SVM models were used to estimate the babies who could have any disease at birth. Sick babies were estimated with an accuracy of 69.5%.
Sms Spam Detection in Turkish Language
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Gürkan, Cem Kaya; Koç, Utku
Short message (SMS) is one of the most common communication methods. The growth of mobile phone users has led to a dramatic increase in using short messages. With the increasing number of mobile phone users, mobile phone users have started receiving unsolicited text messages. The use of SMS as a spam tool after the e-mail is due to a direct access to customer and high reversion to the users. These unsolicited short messages are disturbing the users even content intended for deceiving or defrauding (phishing). Up to date, all of the research carried out on SMS Spam detection was focused on the English language. In this study, Turkish datasets tagged with spam information is introduced and existing methods for English are applied to these datasets. The SMS dataset used in this study is gathered from different people and all messages are tagged according to whether they are spam or not. Naïve Bayes, Logistic Regression, SGD, SVM and Random Forest classification algorithms are tested with three feature extraction methods and a number of performance measures are evaluated. The evaluation resulted in a f-measure of 96.4% for SVM classification algorithm with TF-IDF (Term Frequency-Inverse Document Frequency) extraction method.

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