Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators
Yıl: 2022 Cilt: 4 Sayı: 2 Sayfa Aralığı: 191 - 195 Metin Dili: İngilizce DOI: 10.37990/medr.1021148 İndeks Tarihi: 27-09-2022
Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators
Öz: Aim: It is a known fact that diabetes mellitus is increasing frequently and triggering many different diseases. Therefore, early diagnosis of the disease is important. This study was trying to predict the early diagnosis of the disease, according to machine learning methods by measuring plasma glucose concentration, serum insulin resistance, and diastolic blood pressure.Material and Methods: In the study, the public dataset from a website consists of 768 samples and nine variables. Three different machine learning strategies were used in the early diagnosis of diabetes mellitus (Support Vector Machine, Multilayer Perceptron, and Stochastic Gradient Boosting). 3 repeats and 10 fold cross-validation method was used to optimize the hyperparameters. The model’s performance parameters were evaluated based on accuracy, specificity, sensitivity, confusion matrix, positive predictive value (precision), negative predictive value, and AUC (area under the ROC curve).Results: According to the experimental results (the criteria of accuracy (0.79), sensitivity (0.57), specificity (0.91), positive predictive value (0.79), negative predictive value (0.80), and AUC (0.74)) the Support Vector Machine was more successful than other methods.Conclusion: Plasma glucose concentration, serum insulin resistance, and diastolic blood pressure markers are important indicators in the early diagnosis of diabetes mellitus. In this study, it was seen that these markers make a significant contribution to the early diagnosis of diabetes mellitus. However, it has been observed that these indicators alone will not be sufficient in the early diagnosis of the disease, especially since age, body mass index and pregnancy contribute significantly.
Anahtar Kelime: Plazma Glukoz Konsantrasyonu, Serum Insülin Direnci ve Diastolik Kan Basıncı Göstergeleri ile Makine Öğrenme Yöntemleri Kullanılarak Diyabet Hastalığının Erken Tanısı
Öz: Amaç: Diyabetin sıklıkla arttığı ve bir çok farklı hastalığı tetiklediği bilinen bir gerçektir. Bu nedenle hastalığın erken teşhisi önemlidir. Bu çalışmada plazma glukoz konsantrasyonu, serum insülin direnci ve diyastolik kan basıncı göstergelerinden, makine öğrenmesi yöntemlerine göre hastalığın erken teşhisi öngörülmeye çalışılmıştır.Materyal ve Metot: Çalışmada, bir web sitesinden alınan halka açık veri seti 768 örnek ve dokuz değişkenden oluşmaktadır. Diyabetin erken teşhisinde üç farklı makine öğrenme stratejisi kullanıldı (Destek Vektör Makineleri, Çok Katmanlı Algılayıcılar ve Stokastik Gradyan Artırma). Hiper parametre optimizasyonu için 3 tekrarlı 10 kat tekrarlı çapraz doğrulama yöntemi kullanıldı. Modellerin performansı doğruluk, seçicilik, duyarlılık, karışıklık matrisi, pozitif tahmin değeri (kesinlik), negatif tahmin değeri ve AUC (ROC eğrisi altında kalan alan) temel alınarak değerlendirilmiştir.Bulgular: Deneysel sonuçlara göre (doğruluk (0.79), duyarlılık (0.57), özgüllük (0.91), pozitif tahmin değeri (0.79), negatif tahmin değeri (0.80) ve AUC (0.74) kriterleri), Destek Vektör Makineleri diğer yöntemlere göre daha başarılı çıkmıştır.Sonuç: Diyabet hastalığının erken tanısında plazma glukoz konsantrasyonu, serum insülin direnci ve diastolik kan basinci belirteçleri önemli göstergelerdir. Bu çalışmada da bu belirteçlerin diyabetin erken tanısında önemli katkı sağladığı görülmüştür. Ancak tek başlarına bu göstergelerin hastalığın erken tanısında yeterli olmayacağı özellikle yaş, beden kitle indeksi ve gebeliğin de önemli derecede katkı sağladığı görülmüştür.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | KIVRAK M (2022). Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. , 191 - 195. 10.37990/medr.1021148 |
| Chicago | KIVRAK MEHMET Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. (2022): 191 - 195. 10.37990/medr.1021148 |
| MLA | KIVRAK MEHMET Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. , 2022, ss.191 - 195. 10.37990/medr.1021148 |
| AMA | KIVRAK M Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. . 2022; 191 - 195. 10.37990/medr.1021148 |
| Vancouver | KIVRAK M Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. . 2022; 191 - 195. 10.37990/medr.1021148 |
| IEEE | KIVRAK M "Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators." , ss.191 - 195, 2022. 10.37990/medr.1021148 |
| ISNAD | KIVRAK, MEHMET. "Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators". (2022), 191-195. https://doi.org/10.37990/medr.1021148 |
| APA | KIVRAK M (2022). Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. Medical records-international medical journal (Online), 4(2), 191 - 195. 10.37990/medr.1021148 |
| Chicago | KIVRAK MEHMET Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. Medical records-international medical journal (Online) 4, no.2 (2022): 191 - 195. 10.37990/medr.1021148 |
| MLA | KIVRAK MEHMET Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. Medical records-international medical journal (Online), vol.4, no.2, 2022, ss.191 - 195. 10.37990/medr.1021148 |
| AMA | KIVRAK M Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. Medical records-international medical journal (Online). 2022; 4(2): 191 - 195. 10.37990/medr.1021148 |
| Vancouver | KIVRAK M Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators. Medical records-international medical journal (Online). 2022; 4(2): 191 - 195. 10.37990/medr.1021148 |
| IEEE | KIVRAK M "Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators." Medical records-international medical journal (Online), 4, ss.191 - 195, 2022. 10.37990/medr.1021148 |
| ISNAD | KIVRAK, MEHMET. "Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators". Medical records-international medical journal (Online) 4/2 (2022), 191-195. https://doi.org/10.37990/medr.1021148 |
