Makine Öğrenimi Yöntemleriyle Erken Evre Diyabet Tahmini

Year 2021, Issue: 29, 52 - 57, 01.12.2021

Özge Nur Ergün , Hamza O.ilhan

https://doi.org/10.31590/ejosat.1015816

Cited By: 2

Abstract

Diyabet, tedavisi olmayan, yaygın ve ölümcül bir hastalıktır. Milyonlarca insan diyabet hastasıdır ve bu hastalık hayatlarını doğrudan etkilemektedir. Erken tedavi sayesinde diyabetin etkilerini azaltmak ve hastaların hayat standartlarını arttırmak mümkün olsa da çoğunlukla teşhis konulması yıllar sürebilen bir süreçtir. Diyabetin erken teşhisi için mevcut hastaların verileri kullanılarak makine öğrenmesi uygulanabilir. Bu sayede kan testi, glukoz ölçümü veya bu gibi herhangi bir tıbbi işleme gerek kalmadan diyabet teşhisi konulabilecek, diyabete yakalanma riski olan kişiler saptanabilecektir. Bu yaklaşımla diyabet teşhisinde kullanılabilecek bir makine öğrenmesi modeli geliştirmek çalışmanın konusunu oluşturmaktadır. Sunulan çalışmada 520 hastanın 16 farklı kategoride verisi işlenerek oluşturulan diyabet veri seti üzerinde sekiz makine öğrenmesi yaklaşımı uygulanmış, performans kıyaslaması 10 katlamalı çapraz doğrulama ile doğruluk, kesinlik, duyarlılık ve f skoru metrikleri ile ölçümlenmiştir. Ek olarak veri setinde yer alan özelliklerin diyabet teşhisindeki anlam önceliği araştırılmıştır. Geliştirilen modellerin hepsi belli düzeyde başarı oranını yakalamıştır. En düşük doğruluk oranı %88.82 sınıflandırma başarımı ile basit bir makine öğrenmesi tekniği olan Naive Bayes tekniği kullanılarak elde edilmiştir. En iyi sonuç 1 boyutlu evrişimsel sinir ağı ile elde edilmiştir. Evrişimsel sinir ağı kullanılarak elde edilen modelin doğruluğu %99.04, kesinliği %100, hassasiyet oranı %98.63 ve f skoru %99.31 olarak ölçülmüştür. Elde edilen sonuçlar, geliştirilen sınıflandırmanın diyabet teşhisinde bir soru seti olarak kullanılabileceğini göstermektedir.

Keywords

Diyabet, Makine Öğrenmesi, Diyabet, Makine Öğrenmesi, K-en Yakın Komşu, Destek Vektör Makinesi, Naive Bayes, Karar Ağacı, Rastgele Orman, XGBoost, Yapay Sinir Ağları, Evrişimsel Sinir Ağları

Early Stage Diabetes Prediction Using Machine Learning Methods

Abstract

Diabetes is a common disease that is incurable and fatal. Millions of people worldwide have diabetes and it directly affects people’s lives. Early diagnosis helps reduce the effects of diabetes and improve the life quality of patients, but in common case people live with diabetes for years before getting diagnosed. Early diagnosis can be done by applying machine learning methods on existing data of patients. In this way, people can quickly get diagnosed without taking a glucose screening test or any blood test. Answering a simple question set would be enough to determine if a person is diabetic or has a risk of being diabetic. In the proposed study, determination of diabetes is performed by machine learning techniques. In this scope, a publicly available diabetes dataset, which includes 16 features that are collected from 520 people, was used to create predictive models. Eight machine learning methods were individually performed over the dataset. The results of each model were validated by using a 10 fold cross validation schema. Addition to accuracy metric, confusion matrix based other performance metrics; precision, recall and f1 score, were also reported. All of the created models resulted in high accuracy scores. The minimum accuracy score was measured as 88.85% by using one of the basic machine learning techniques, Naive Bayes. The highest accuracy rate was 99.04%, which is obtained by using a one dimensional convolutional neural network model. The designed Convolutional Neural Network model also resulted in highest performance scores for other metrics as 100.00%, 98.63% and 99.31% for precision, recall and f1 scores, respectively. These findings indicate that the created 1D CNN model can be utilized in the determination of diabetic patients by asking only several questions to patients.

Keywords

Diabetes, Machine Learning, K Nearest Neighbor, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, XGBoost, Artificial Neural Network, Convolutional Neural Network

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