Supervised Learning based Chest Disease Prediction with IOT from Xray Images

New WHO research indicates that there are an increasing number of chest-related illnesses. This results in the deaths of 17.9 million people annually. It gets harder to identify problems and start therapy at an early age as the population grows. However, new developments in technology, such as deep learning and machine learning methods, have sped up research in the medical profession. The creation of a machine learning and deep learning model for heart disease prediction based on pertinent features is the goal of this study. The chest's X-ray images are kept in the cloud for public access in the suggested method. The images retrieved from the cloud via the internet are analyzed for prediction using machine learning methods such as K Nearest Neighbor and Random Forest and deep learning algorithms such as Convolutional Neural Network and ResNet. Results are further stored in the cloud. Doctors, users, and patients can access the saved findings on cloud servers for diagnosis and treatment. We used a benchmark dataset from UCI Chest Disease Prediction for this research study. The proposed method classified X-ray images into normal and chest disease as cardiomegaly, aortic enlargement, and enlarged cardioment. Results are updated in the cloud for doctor diagnosis purposes. From experimental analysis, ResNet produced better results compared to other methods.