Machine Learning Based Detection of Foot Ulcer and Diabetic Foot Using IoT

Millions of individuals worldwide suffer from diabetes mellitus, a chronic metabolic condition. Diabetes can cause foot ulcers, which are one of the most dangerous side effects. If left untreated, these ulcers might result in amputation. Both healthy and ulcerated feet from diabetes patients make up the sizable dataset used to train the machine learning models. Based on the sensor data gathered from the setup, the trained models are then used to make real time predictions about the risk of ulcer formation. This study offers a novel method for detecting diabetic foot ulcers through the use of machine learning (ML) and the internet of things (IoT). This paper introduces a new, noninvasive, and reasonably priced method for detecting diabetic foot ulcers and diabetes using NodeMCU, MPU6050, Flex, MAX30100, and DHT11 sensors, as well as piezoelectric crystal sensors. The technology offers a tool for early disease detection, allowing for prompt intervention and the avoidance of consequences. This system offers a potential approach to health diagnosis and management by utilizing technology and machine learning, which enhances quality of life and lowers healthcare expenses.