A Comprehensive Web Application for Chronic Kidney Disease Prediction with Cuisine-Centric Diet Recommendation

Despite advancements in detection and treatment, there exists a critical gap in patient care-post-diagnosis guidance and support. In response to this challenge, our proposed work presents a novel integrated system designed to guide and empower CKD patients towards a healthier and more fulfilling life. The proposed work aims at creating an integrated system for the early detection of chronic kidney disease (CKD) using Machine Learning technology, a diet recommender for the prevention of disease from further development. The model output is integrated with the Flask framework and the front end, which is developed in HTML (includes Java Script) is used to receive user input on various parameters needed to decide on the early detection of kidney disease. This model is deployed into the IBM cloud using API keys and scoring endpoints. The chronic kidney disease prediction and diet recommendations are displayed in a Kidney Guard web application that has multiple user-engagement features such as a motivator chatbot, homemade paradise hub (for homemade recipes), prediction history, calorie recorded and burnt, soul stretch (for yoga) and sweat spot (for exercise). This work enables doctors in the early detection of CKD and cuisine-centric diet recommendation helps the users to adhere to the dietary and lifestyle changes which are holistic components in the prevention and management of CKD.