Deployment of Disease Prediction Model in AWS Cloud

More than 500,000 humans go to emergency rooms every year for kidney stone problems. One out of each ten humans will broaden a kidney stone sooner or later in their lives. In India, kidney stones are one of the most common diseases which can be fatal if not treated properly. It can be caused by various parameters making it even more difficult to treat. When kidney stones are discovered in their early stages, they are much easier to treat than when they are discovered later on. To help this purpose, this study aims the development a website that is capable of predicting the presence of kidney stones using an image that was uploaded by the user itself. This website serves as a preliminary screening tool for the detection of kidney stones. This website is backed up by the algorithm which is proven to be the best in the prediction of kidney stones after a comparison between two different algorithms. These algorithms are trained and tested using the dataset which was obtained from Kaggle. This dataset is preprocessed to ensure the best performance of the classifier models. The performance of both the models is then compared and it is found that theSupport Vector Machine (SVM) algorithm is better than the Logistic Regression (LR) algorithm. The website is also integrated with the cloud using the AWS platform. This ensures the presence of an eternal space that supports the website when the number of users of the website increases.