Açıklanabilir Yapay Zeka Teknikleri ile Fotopletismografi (PPG) Sinyalleri Kullanarak Kan Glikoz Seviyesi Tahmini

Year 2024, , 45 - 62, 29.06.2024

Gökhan Adigüzel , Ümit Şentürk , Kemal Polat

https://doi.org/10.56171/ojn.1473276

Abstract

Kan şekeri seviyelerinin tahmini, diyabetin etkili yönetiminde kritik bir görevdir. Bu çalışma, Fotopletismografi (PPG) sinyallerini kullanarak kan şekeri seviyelerini tahmin etmek için CatBoost, XGBoost ve ekstra ağaç regresör gibi makine öğrenimi modellerinin gücünden, SHAP değerleri ve karışıklık matrisi gibi açıklanabilir yapay zeka teknikleriyle birlikte yararlanmaya odaklanıyor. Bu araştırmada kullanılan veri seti, PPG sinyallerinden glikoz tahmini için dikkatlice seçilmiştir ve 217 kişiden alınan verilerden oluşmaktadır. Her bireyin bilgileri, laboratuvar glikoz ölçümlerini ve yaklaşık bir dakikalık kaydedilen parmak PPG sinyallerini içerir. Test edilen çeşitli makine öğrenimi modelleri arasında CatBoost, kan şekeri seviyelerini tahmin etmede en iyi performansı gösteren model olarak ortaya çıktı. CatBoost modeli, 0.7191'lik etkileyici bir determinasyon katsayısı (R2) metriğine ve 25.21'lik ortalama mutlak hataya (MAE) ulaşarak glikoz seviyesi tahminlerindeki verimliliğini ve doğruluğunu ortaya koydu. Özellik önemi analizi, CatBoost ile oluşturulan tahmin modelinde medyan fark ve basıklık gibi belirli özelliklerin önemini vurgulayarak bunların kan şekeri seviyelerinin belirlenmesindeki önemli rolünün altını çizdi. Açıklanabilir yapay zeka tekniklerinin dahil edilmesi, tahmine dayalı modellerin yorumlanabilirliğini ve şeffaflığını arttırdı. Sonuç olarak bu araştırma, PPG sinyallerinden kan şekeri seviyelerinin tahmin edilmesinde makine öğrenimine dayalı yaklaşımların potansiyelini vurgulamaktadır. CatBoost gibi gelişmiş modellerden yararlanan ve açıklanabilir yapay zeka yöntemlerini kullanan bu çalışma, doğru, invaziv olmayan ve veriye dayalı tahmine dayalı metodolojiler yoluyla gelişmiş diyabet yönetiminin yolunu açıyor.

Keywords

Kan Şekeri Tahmini, Fotopletismografi, Makine Öğrenmesi, SHAP, Açıklanabilir Yapay Zeka

Blood Glucose Level Estimation Using Photoplethysmography (PPG) Signals with Explainable Artificial Intelligence Techniques

Abstract

Estimating blood sugar levels is a critical task in effective diabetes management. This study focuses on leveraging the power of machine learning models such as CatBoost, XGBoost, and Extra Trees Regressor, along with explainable AI techniques like SHAP values and confusion matrices, to predict blood sugar levels using Photoplethysmography (PPG) signals. The dataset used in this research is carefully selected for glucose prediction from PPG signals and consists of data from 217 individuals. Information for each individual includes laboratory glucose measurements and approximately one minute of recorded finger PPG signals. Among the various machine learning models tested, CatBoost emerged as the best-performing model in predicting blood sugar levels. The CatBoost model demonstrated its efficiency and accuracy in glucose level predictions by achieving an impressive coefficient of determination (R2) of 0.7191 and a mean absolute error (MAE) of 25.21. Feature importance analysis highlighted the significance of specific features like median deviation and kurtosis in the predictive model built with CatBoost, emphasizing their critical role in determining blood sugar levels. The inclusion of explainable AI techniques enhanced the interpretability and transparency of predictive models. In conclusion, this research underscores the potential of machine learning-based approaches in predicting blood sugar levels from PPG signals. By leveraging advanced models like CatBoost and utilizing explainable AI methods, this study paves the way for improved diabetes management through accurate, non-invasive, and data-driven predictive methodologies.

Keywords

Blood Sugar Prediction, Photoplethysmography, Machine Learning, SHAP, XAI

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