Optimized machine learning based predictive diagnosis approach for diabetes mellitus

Year 2023, Volume: 4 Issue: 4, 270 - 276, 30.08.2023

Erkan Akkur , Fuat Türk

https://doi.org/10.47582/jompac.1307319

Abstract

Aims: Diabetes mellitus is a metabolic disease caused by elevated blood sugar. If this disease is not diagnosed on time, it has the potential to pose a risk to other organs and tissues. Machine learning algorithms have started to preferred day by day in the detection of this disease, as in many other diseases. This study suggests a diabetes prediction approach incorporating optimized machine learning (ML) algorithms.
Methods: The framework presented in this study starts with the application of different data pre-processing processes. Random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT) algorithms are used for classification. Grid search is utilized for hyperparameter optimization of algorithms. Different performance evaluation measures are used to find the algorithm that best predicts diabetes. PIMA Indian dataset (PID) is chosen for testing the experiments. In addition, it is investigated to what extent the attributes in the data set affect the result using Shapley additive explanations (SHAP) analysis.
Results: As a result of the experiments, the RF algorithm achieved the highest success rate with 89.06%, 84.33%, 84.33%, 84.33% and 0.88% accuracy, precision, sensitivity, F1-score and AUC scores. As a result of the SHAP analysis, it is found that the “Insulin”, “Age” and “Glucose” attributes contributed the most to the prediction model in identifying patients with diabetes.
Conclusion: The hyperparameter optimized RF approach proposed in the framework of the study provided a good result in the prediction and diagnosis of diabetes mellitus when compared with similar studies in the literature. As a result, an expert system can be designed to detect diabetes early in real time using the proposed method.

Keywords

Machine learning, diabetes mellitus, data preprocessing, grid search, random forest

Diabetes mellitus için optimize edilmiş makine öğrenimi tabanlı öngörücü tanı yaklaşımı

Abstract

Amaç: Diabetes mellitus, kan şekeri yüksekliğinin neden olduğu metabolik bir hastalıktır. Bu hastalık zamanında teşhis edilmezse diğer organ ve dokular için risk oluşturma potansiyeline sahiptir. Diğer birçok hastalıkta olduğu gibi bu hastalığın tespitinde de makine öğrenimi algoritmaları gün geçtikçe tercih edilmeye başlandı. Bu çalışma, optimize edilmiş makine öğrenimi (ML) algoritmalarını içeren bir diyabet tahmin yaklaşımı önermektedir.
Gereç ve Yöntem: Bu çalışmada sunulan çerçeve, farklı veri ön işleme süreçlerinin uygulanmasıyla başlamaktadır. Sınıflandırma için rastgele orman (RF), destek vektör makinesi (SVM), K-en yakın komşu (K-NN) ve karar ağacı (DT) algoritmaları kullanılmaktadır. Grid arama, algoritmaların hiperparametre optimizasyonu için kullanılır. Diyabeti en iyi tahmin eden algoritmayı bulmak için farklı performans değerlendirme ölçütleri kullanılır. Deneylerin test edilmesi için PIMA Indian veri seti (PID) seçilmiştir. Ayrıca Shapley toplamsal açıklamalar (SHAP) analizi kullanılarak veri setindeki özniteliklerin sonucu ne ölçüde etkilediği araştırılmıştır.
Bulgular: Yapılan deneyler sonucunda RF algoritması %89,06, %84,33, %84,33, %84,33 ve %0,88 doğruluk, kesinlik, hassasiyet, F1-skoru ve AUC puanları ile en yüksek başarı oranına ulaşmıştır. SHAP analizi sonucunda diyabetli hastaların belirlenmesinde tahmin modeline en çok "İnsülin", "Yaş" ve "Glikoz" özniteliklerinin katkı sağladığı bulunmuştur.
Sonuç: Çalışma çerçevesinde önerilen hiperparametre optimize edilmiş RF yaklaşımı, literatürdeki benzer çalışmalarla karşılaştırıldığında diabetes mellitusun öngörü ve tanısında iyi bir sonuç sağlamıştır. Sonuç olarak, önerilen yöntem kullanılarak diyabetin gerçek zamanlı olarak erken saptanması için bir uzman sistem tasarlanabilir.

Keywords

Machine learning, diabetes mellitus, data preprocessing, grid search, random forest

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