ML based IoT Framework for Diabetes Detection

Abstract There has been a discernible increase in the prevalence of diabetes in recent years, highlighting the significance of early detection in successfully managing the condition and avoiding complications. A chronic disease called diabetes is characterized by persistently elevated blood sugar levels. This requires consistent monitoring, medication, lifestyle modifications, and adherence to a healthy diet. The system's ability to detect diseases early can help with quick treatment and illness management. The platform makes use of Internet of Things (IoT) technology to provide a simple and quick way to monitor patients' health while reducing difficulties caused by diabetes. Several machine learning algorithms have been used to differentiate between diabetes and non-diabetic patients, including Adaboost, Gridsearch, Evalml, AutoML, and Artificial Neural Network. This research introduces a machine learning model-based IoT system for diabetes detection. The suggested system combines Internet of Things (IoT) devices for gathering physiological data with a cloud-based platform for processing and analyzing the data. It was determined through meticulous investigation that hyperparameter modification greatly improved the performance of the aforementioned algorithms, with the Random Forest algorithm showing the highest accuracy. The IoT-enabled technology offers a trustworthy and affordable option for keeping track of diabetic patients' health, enabling early diagnosis and effective treatment of the condition. Overall, the research's findings highlight the potential for machine learning to improve healthcare outcomes for people with diabetes by shedding light on the role it plays in the detection and management of the disease.