22 Ekim 2022 Konferans bildirisi Açık Erişim

İsmail Buğra Bölükbaşı; Betül Yağmahan

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    <subfield code="a">Diabetes Diagnosis</subfield>
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    <subfield code="a">Type-2 Diabetes</subfield>
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    <subfield code="a">&lt;p&gt;In recent years, the number of people with diabetes has been increasing daily. Diabetes is an important&lt;br&gt;
disease that can cause serious damage to the body in the future and even cause death if precautions are&lt;br&gt;
not taken. Early and accurate detection of ever-increasing diabetes is gaining more importance in the&lt;br&gt;
medical world. The number of studies using machine learning methods to diagnose diabetes has&lt;br&gt;
increased significantly in the literature.&lt;br&gt;
In this study, type-2 diabetes disease was classified using different data preprocessing and machine&lt;br&gt;
learning methods on real-world data taken from a public hospital in Turkey. Logistic regression, Naive&lt;br&gt;
Bayes, C4.5, and Random Forest classification models were used in the study. In the classification&lt;br&gt;
models, the patient&amp;#39;s age, gender, complete blood count, biochemistry, and hormone test results were&lt;br&gt;
used as input variables, and the disease diagnosis made by specialist doctors was used as output variable.&lt;br&gt;
In total, 43 different variables were studied. When the dataset was examined, it was noticed that there&lt;br&gt;
was an imbalance between the classes in the target variable. In cases where there is a class imbalance,&lt;br&gt;
the classification models can make incorrect assignments to the classes. To eliminate the class imbalance&lt;br&gt;
in the data set used in the study, three different resampling methods were used: random undersampling&lt;br&gt;
(RUS), random oversampling (ROS), and synthetic minority oversampling (SMOTE).&lt;br&gt;
The performances of four different machine learning methods were compared on each of the original&lt;br&gt;
training dataset, random undersampled training dataset, random oversampled training dataset, and&lt;br&gt;
synthetic minority oversampled training dataset. A total of 16 different scenarios were studied.&lt;br&gt;
As a result of the analysis of all scenarios, four combinations that give the best results were determined.&lt;br&gt;
These are Naive Bayes working with original training dataset, Random Forest working with random&lt;br&gt;
undersampled training and synthetic minority oversampled training datasets, and C4.5 algorithm&lt;br&gt;
working with random oversampled training dataset. The algorithm that takes the first place among the&lt;br&gt;
four scenarios that show the best results is the Random Forest algorithm working with random&lt;br&gt;
undersampled training dataset.&lt;/p&gt;</subfield>
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    <subfield code="a">CUKUROVA 9th INTERNATIONAL SCIENTIFIC RESEARCHES CONFERENCE</subfield>
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    <subfield code="a">İsmail Buğra Bölükbaşı</subfield>
    <subfield code="u">Yalova Üniversitesi</subfield>
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    <subfield code="u">https://aperta.ulakbim.gov.trrecord/286136/files/DIAGNOSIS OF DIABETES DISEASE USING MACHINE LEARNING METHODS IN AN IMBALANCED DIABETES DATASET.pdf</subfield>
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