Diabetes emergency cases identification based on a statistical predictive model

Abstract

Diabetes is a chronic metabolic disease which is characterized by a permanently high blood sugar level. A distinction is made between two forms: Type 1 diabetes and Type 2 diabetes. It is believed that there are around 415 million people between the ages of 20 and 79 worldwide who have some form of diabetes illness today. In Europe, over 60 million people are diabetic, a diabetes incidence of 10.3% of men and 9.6% of women is estimated. The prevalence of diabetes is increasing among all ages in the European Region, mainly due to increases in overweight and obesity, unhealthy diet, and physical inactivity. A huge people in this population have type 2 diabetes, and the numbers will continue to rise over the next few years. So one can speak of a real widespread disease. The problem is not only the increased blood sugar, but also complications and accompanying diseases such as heart attack, stroke, or diabetic foot. However, as a type 2 diabetic, we can significantly influence the course of the disease and the success of therapy. To do this, it is important that we early detect the person that have (or likely have) a serious problem or an emergent case, and know about it as fast as possible. Early detection and treatment of this disease are very important to help diabetics live a healthy and near normal life. It can also help to avoid several serious complications. In addition, the evolution of wearable and Internet of Things medical devices can help to collect various health data for diagnosis using machine learning algorithms. In this paper, we present an IoT-based system architecture which ensures the collection of patient data in order to predict serious cases of diabetes. To secure data, Blockchain and IPFS are used, and to analyze data, we propose a statistical-based method for predictions. The process is as follows. First, data were collected from IoT devices, and a dataset was constructed and stored using IPFS. Then, the data will be scaled and filtered using noise-invariant data expansion. Next, an adaptive random forest algorithm is made in order to train data on the training dataset, and people with diabetes were classified using the proposed model. Three datasets were used, namely, the Pima Indian diabetes dataset, the Frankfurt Hospital diabetes dataset, and the last is the fusion of these two datasets. Finally, the performance of the method was evaluated and compared with other recent prediction methods. Based on the experiment result, an accuracy of 85.9%, 99.5%, and 99.8% has been achieved based on the three datasets, respectively. Thus, the model can be used to predict and alert physicians or hospitals serious cases that need urgent reactions.