Study on IOT based Smart Disease Detection Model on Machine Learning Techniques for Healthcare Applications

The wearable medical devices are integrated with the internet of things (IoT) for smart healthcare applications to improve the quality of service. The information regarding the patient’s details updated through the IoT devices helps to provide the medication based on the present condition and hence the severity can be reduced instantly. Machine learning (ML) is utilized for making better decisions through clustering and classification methods that efficiently deal with the quality of information. Hence, this research introduces an analysis of smart disease prediction techniques for healthcare applications. The analysis of diseases such as heart disease, Alzheimer’s disease, and diabetic retinopathy disease based on machine learning is employed by reviewing the conventional detection methods along with the achievements and research gaps. In addition, the analysis based on the performance metrics, dataset, and tools utilized is also devised for the development of the novel smart disease detection technique with more efficient accuracy of detection.