Yapay Zeka Tabanlı Akıllı Telefon Uygulaması ile Kan Şekeri Tahmini

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 289 - 294, 31.07.2021

Volkan Kılıç

https://doi.org/10.31590/ejosat.950914

Cited By: 1

Abstract

Diyabet, insan vücudunda birçok hastalığı tetikleyen kronik bir hastalıktır. İnsan vücudunu olumsuz etkileyen diyabet için kritik şeker seviyeleri, hiperglisemi (yüksek kan şekeri) ve hipoglisemi (düşük kan şekeri) olarak adlandırılır. Kan şekerinin bu seviyelerin üstünde veya altında olması insan vücudunda çeşitli tahribatlara neden olmaktadır. Mevcut sistemler, kan şekerini sürekli ölçerek takip yapmakta olup, kritik seviyeler geçildikten sonra kullanıcıya uyarı vermektedir. Kullanıcının uyarının farketmesi, gerekli ilaç ve/veya tedbirleri almasına takiben kan şekeri istenilen seviyeye indirilebilmektedir. Bu durum, hasta vücudunun belirli bir süre kritik seviyelerde çalışmasına neden olmaktadır. Bu sorunun önüne geçebilmek için, kan şekerinin zaman içindeki değişiminden bir sonraki kan şekerinin değerini tahmin edebilecek ve böylelikle, kritik seviyelerine ulaşmadan hastayı uyarabilecek bir sistem geliştirilmiştir. Önerilen sistem, uzun-kısa süreli bellek (Long Short Term Memory-LSTM) tabanlı tekrarlayan sinir ağı (Recurrent Neural Network-RNN) ile zamanla değişen kan şekeri değerlerinden bir sonraki değeri tahmin edebilmektedir. Diyabet hastalarından elde edilen gerçek veriler ile eğitilen sistem, %95.6 doğruluğa karşılık gelen 3.72 mg/dl'den daha düşük bir hata ile kan şekerini tahmin edebilmiştir. Önerilen sistem, ayrıca, kendi geliştirdiğimiz BffDiabetes adlı Android uygulamamızla birleştirilmiştir. Uygulama, ölçülen kan şekeri değerini bir bulut sistemi üzerinden bir sonraki şeker seviyesini tahmin etmek için sunucuya gönderir. Sunucuda koşturulan LSTM tabanlı tahmin algoritmamız gelen değere bağlı olarak gelecek üç zaman adımı için şeker değerlerini hesaplar. Tahmin sonuçları, kan şekeri seviyesinin kritik aşamalara ulaşma eğiliminde olup olmadığını değerlendirmek için bulut sistemi üzerinden tekrar Android uygulamasına gönderilir. Bu eğilim tespit edilirse, uygulama hastayı gerekli önlemler için bir bildirimle uyarır. Böylelikle, kan şekerini ölçerek kablosuz (Bluetooth) aktarım yapan cihazlarla çalışabilecek bir platform diyabet hastalarının kullanımına sunularak günlük hayat kalitelerinin artırılması amaçlanmıştır.

Keywords

Yapay zeka, Tekrarlayan Sinir Ağı, LSTM, Akıllı Telefon, Android

Artificial Intelligence based Blood Sugar Prediction with Smartphone Application

Abstract

Diabetes is a chronic disease that triggers many diseases in the human body. Critical sugar levels for diabetes, which negatively affects the human body, are referred to as hyperglycemia (high blood sugar) and hypoglycemia (low blood sugar). Blood sugar being above or below these levels causes various damages in the human body. Existing systems monitor blood sugar continuously, and alert the user after critical levels are passed. After the user notices the warning and takes the necessary medication and/or precautions, the blood sugar can be reduced to the desired level. This situation causes the patient's body to work at critical levels for a certain period of time. In order to avoid this problem, a system has been developed that can predict the value of the next blood sugar from the change in blood sugar over time and thus alert the patient before reaching critical levels. The proposed system can predict the next value from the time-varying blood sugar values with the Long Short Term Memory (LSTM) based recurrent neural network (RNN). Trained with real data from diabetes patients, the system was able to predict blood sugar with an error of less than 3.72 mg/dl, corresponding to 95.6% accuracy. The proposed system is also combined with our custom-designed Android application called BffDiabetes. The application sends the measured blood sugar value via a cloud system to the server to predict the next sugar level. Our LSTM-based prediction algorithm run on the server calculates sugar values for the next three time steps based on the incoming value. The prediction results are sent back to the Android app via the cloud system to assess whether the blood sugar level tends to reach critical stages. If this trend is detected, the application alerts the patient with a notification for necessary precautions. In this way, it is aimed to increase the quality of daily life by offering a platform that can work with devices that make wireless (Bluetooth) transmission by measuring blood sugar.

Keywords

Artificial Intelligence, Recurrent Neural Network, LSTM, Smartphone, Android

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