Retracted: Lightweight Machine Learning Algorithm for Automatic Detection of Diabetic Retinopathy in IoT

One of the main leading causes of blindness in the industrialized regions is diabetic retinopathy (DR), an eye condition. An automatic method for early diagnosis and treatment to prevent vision is becoming more and more necessary since unhealthy lifestyles are being adopted and the number of diabetics is increasing more quickly. The advancement of various technology, such as smart devices, cloud computing, and the Internet of Things (IoT), has made it possible to diagnose and treat patients remotely, reducing the requirement for hospitalization and enabling the delivery of health care to large populations. The presented method uses Dexcom G4 Plantinum sensors onto diabetic patients to assess blood glucose levels. The Internet of Things (IoT) technology offers a long-term remedy to diabetic retinopathy, according to the findings. This study aims to prevent eyesight loss from endangering the patient's life. The IoT infrastructure employed in the gadgets for secure data transmission, providea universal language for mobile apps to communicate with one another is where the process begins. This platform continuously collects a huge amount of data from the device and stores it in a safe database. It takes into account the information obtained from IoT devices and employs analytics to foretell important information to meet healthcare demands. In terms of reliability, selectivity, and sensitivity, the outcomes produced by using the presented methodologies are on par with those of complex systems. By reaching an average 99.58 percent Precision, 99.51 percent Sensitivity, and 99.83 percent Specificity in the experimental procedure, the presented methodology outperforms other strategies.