Çok Katmanlı LSTM Kullanarak Çok Parametreli Glikoz Tahmini

Öz

Diyabet, hastaların yaşam kalitesini etkileyen hiperglisemi (yüksek glikoz) ve hipoglisemi (düşük glikoz) gibi düzensiz glikoz seviyelerine neden olmaktadır. Hiperglisemi ve hipogliseminin erken teşhisi bu hastalığın etkin yönetimi için önemlidir. Son yıllarda, etkili diyabet yönetimi için yapay zeka tabanlı araçların geliştirilmesinde ilerleme kaydedilmiştir. Bu araçlar, glikoz seviyelerini kritik seviyelere ulaşmadan önce tahmin etmeyi ve diyabetli kişilerin glikoz seviyelerini sağlıklı bir aralıkta tutmak için proaktif önlemler almalarını sağlamayı amaçlamaktadır. Ancak, bu araçların çoğu tek katmanlı mimariler kullanmakta ve tahmin parametresi olarak yalnızca glikoz ölçümüne dayanmakta, dolayısıyla düşük tahmin doğruluğu ile sonuçlanmaktadır. Bu makalede, glikoz tahmini için çok katmanlı Uzun-Kısa Süreli Bellek (LSTM) tabanlı bir model önerilmektedir. Önerilen model OhioT1DM veri kümesi üzerinde test edilmiş ve 30 dakikalık bir tahmin ufku boyunca glikoz tahmini için en düşük Kök Ortalama Kare Hata değeri 14.364 mg/dL olarak elde edilmiştir. Sonuçlar, çok parametreli tahminle ilişkili karmaşık bellek işlemlerinin üstesinden gelmek için çok katmanlı bir LSTM algoritması kullanan önerilen sistemin performansını göstermektedir.

Anahtar Kelimeler

• Yapay Zeka
• LSTM
• Diyabet
• Glikoz Tahmini

Multi-Parametric Glucose Prediction Using Multi-Layer LSTM

Abstract

Diabetes causes irregular glucose levels, such as hyperglycemia (high glucose) and hypoglycemia (low glucose), which affect the quality of life of diabetes patients. Early detection of hyperglycemia and hypoglycemia is important for effective management of the disease. In recent years, progress has been made in the development of artificial intelligence-based tools for effective diabetes management. These tools aim to predict glucose levels before they reach critical levels, enabling people with diabetes to take proactive measures to keep their glucose levels within a healthy range. However, most of these tools use single-layer architectures and rely only on glucose measurement as a predictive parameter, thus resulting in low predictive accuracy. Here, this paper proposes a multi-layer Long-Short Term Memory (LSTM)-based model for glucose prediction. The proposed model was tested on the OhioT1DM dataset and the lowest Root Mean Square Error value was obtained as 14.364 mg/dL for glucose prediction over a 30-min prediction horizon. The results demonstrate the performance of the proposed system, which uses a multi-layer LSTM algorithm to overcome the complex memory operations associated with multi-parameter prediction.

Keywords

• Artificial Intelligence
• LSTM
• Diabetes
• Glucose Prediction

Kaynakça

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