An IoT based Solution for Predicting Diabetes using Machine Learning

A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. One major cause of DBD (hyper-glycemia) is the deficiency of insulin and beta cells in the pancreas produced insufficient insulin which is called type-1 DB. In type-2 DBD, the body cannot use the produced insulin accordingly. 

The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning to detect and classify the presence of diabetes disease in e- healthcare environment. We have proposed a filter method based on the Decision Tree algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as AdaBoost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also.

Keywords: IoT, Decision Tree, AdaBoost, Random Forest, Machine Learning.