Diyabet hastalığı teşhisinde makine öğrenimi modelleri ile açıklanabilir yapay zeka yöntemlerinin analizi

Öz

Sağlık sektöründe son yıllarda büyük veri kümelerinin artmasıyla birlikte, makine öğrenimi yöntemleri diyabet veri setlerinin keşfi, analizi ve tahmin süreçlerinde önemli bir rol oynamaya başlamıştır. Bu çalışmada, diyabetin erken teşhisine odaklanılmış ve çeşitli makine öğrenimi modellerinin performansı ile Açıklanabilir Yapay Zeka (XAI) tekniklerinin etkisi incelenmiştir. Diyabet veri seti üzerinde, K-En Yakın Komşular (KNN), Destek Vektör Makineleri (SVM), Naive Bayes, Yapay Sinir Ağları (YSA), Karar Ağaçları, Rastgele Orman ve XGBoost algoritmaları kullanılarak modellerin performansları karşılaştırılmıştır. Modellerin değerlendirilmesi, veri temizleme ve ön işleme aşamalarının ardından eğitim ve test süreçleriyle yapılmıştır. Bu değerlendirme sırasında doğruluk, F1 skoru, hassasiyet ve özgünlük gibi başarı kriterleri göz önünde bulundurulmuştur. En iyi sonuç veren modelin çıktılarına SHAP (Shapley Additive Explanations) ve LIME (Local Interpretable Model-Agnostic Explanations) gibi açıklanabilir yapay zeka yöntemleri uygulanarak, modelin verdiği kararların anlaşılabilirliği sağlanmıştır. Bu yöntemler, modelin en önemli özelliklerini belirleyip karar sürecini daha şeffaf hale getirir. Gerçek dünya uygulamalarında bu modellerin potansiyelini anlamak adına uzman görüşleriyle analiz sonuçları desteklenmiştir. KNN, SVM, Naive Bayes, YSA, Karar Ağaçları, Rastgele Orman ve XGBoost algoritmaları sırasıyla %81.18, %75.38, %75.49, %74.83, %76.91, %91.68 ve %98.91 doğruluk oranlarıyla değerlendirilmiştir.

Anahtar Kelimeler

• Açıklanabilir Yapay Zeka
• Diyabet
• SHAP
• XGBoost

In-depth analysis of machine learning models and explainable artificial intelligence methods in diabetes diagnosis

Öz

With the increase in large datasets in the healthcare sector in recent years, machine learning methods have started to play an important role in the discovery, analysis and prediction processes of diabetes datasets. This study focused on the early diagnosis of diabetes and examined the performance of various machine learning models and the effect of Explainable Artificial Intelligence (XAI) techniques. The performances of the models were compared using K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes, Artificial Neural Networks (ANN), Decision Trees, Random Forest and XGBoost algorithms on the diabetes dataset. The evaluation of the models was carried out with training and testing processes after the data cleaning and preprocessing stages. During this evaluation, success criteria such as accuracy, F1 score, sensitivity and originality were taken into consideration. Explainable artificial intelligence methods such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to the outputs of the model that gave the best results, and the understandability of the decisions made by the model was ensured. These methods determine the most important features of the model and make the decision process more transparent. In order to understand the potential of these models in real-world applications, the analysis results were supported by expert opinions. KNN, SVM, Naive Bayes, ANN, Decision Trees, Random Forest and XGBoost algorithms were evaluated with 81.18%, 75.38%, 75.49%, 74.83%, 76.91%, 91.68% and 98.91% accuracy rates, respectively.

Anahtar Kelimeler

• Diabetes
• Explainable Artificial Intelligence
• SHAP
• XGBoost

Kaynakça

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