CI-DPF: A Cloud IoT based Framework for Diabetes Prediction
Parampreet Kaur, Neha Vaishnavi Sharma, Ashima Singh, Bob Gill · 2018
Smart healthcare technology is one of the highest explored areas which apply modern computing technologies and techniques in healthcare research. By making use of sensors in smart wearable devices, the patient-generated data can be sent to electronic devices or any health records. This enables doctors/caregivers to directly monitor the patient activity in real-time. Moreover, a high volume of medical information is continuously produced with every passing day. It is an intrinsic need to gather, store and learn from the medical data to predict health of such patients. An alarming increase in the number of diabetic patients has become an important area of concern for medical researchers. In this paper, a Cloud IoT based framework for diabetes prediction is proposed and presented. It incorporates sensors in smart wearable devices as set of connected IoT devices for continuous monitoring and collections of blood glucose data which is sent for storage in cloud environment where an ensemble model is used to predict diabetes in patients. Experiment on ten Ensemble models by pairing two out of five different machine learning methods is carried out. The ensemble model of Decision Tree and Neural Network achieved the highest accuracy of 94.5% when evaluated using “Pima Indians Diabetes” data set.