Mühendislik Fakültesi / Faculty of Engineering

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

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Start date: 2020Subject: search.filters.subject.machine learning

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    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.
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    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%.
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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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    Idfatification Using FCG Signals
    (2020) Kilicer, Elif Cansu; Ay, Sevval; Aksahin, Mehmet Feyzi
    Systems that determine identity with individual features are called biometric systems. Today, voice, fingerprint, retina/iris, and facial recognition systems are some of the biometric identification methods. These methods have become replicable with the advancement of technology. Accordingly, Electrocardiogram (ECG) signals are universal, unique, easy to measure, and can only be obtained from living people. For this reason, it can be accepted that ECG is an effective method that can he used to prevent counterfeiting among biometric identification methods. In this study, an algorithm that can make identification via ECG is proposed. Within the scope of the study, the time and time-frequency domain analyzes of the ECG signals obtained from the PhsiyoNet database are performed then various features are determined. These determined features were classified using machine learning methods. The performance of the developed algorithm has been calculated as 100% accuracy, 1000/0 specificity, and 100% sensitivity.
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    Predicting Infections Using Computational Intelligence - A Systematic Review
    (2020) Baldominos, Alejandro; Puello, Adrian; Ogul, Hasan; Asuroglu, Tunc; Colomo-Palacios, Ricardo; 0000-0003-4153-0764
    Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
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    Daphnet Freezing Recognition with Gait Data by Using Machine Learning Algorithms
    (2020) Guney, Selda; Boluk, Busra
    The aim of this study was to test the success of the data set obtained by a wearable health assistant developed for the symptom of freezing (FOG) in gait of Parkinson's patients and to increase the success of the system. The system was tested with different machine learning methods to measure the success of the wearable health assistant system. For all patients (ten patients), the highest success value was obtained and the mean sensitivity and specificity values of the system were calculated and compared with the results obtained in the literature review. In the literature, mean sensitivity and specificity were 73.1% and 81.6%, respectively; In this study, mean sensitivity and specificity were 91.9% and 71.14%, respectively. In order to better analyze the success of the system, two patients with successful and unsuccessful results were selected for the data set in line with the results obtained in the literature review. The success of the system was tested by using different machine learning methods on the data sets of two patients. Finally, the successes obtained by feature extraction methods were tried to be increased. Among the different machine learning methods on the data sets used for patient 8 and patient 3, the most successful method was obtained by combining the models (ensemble). The highest achievement value obtained by attribute extraction methods was obtained when PCA was applied. However, the success value obtained with raw data could not be increased. All results are tabulated and presented.