AI-powered Health Care System using Machine Learning and Deep Learning - Chapa Krishnaveni; Etlam Poojith Reddy; Panjala Nikhila; Ranjitha R; S K Hiremath (International Journal of Data Science, Bioinformatics and Cyber Security, 2026)
The web app is a Health Assistance Web Application that helps people get healthcare advice in a way that makes it easier to find, trust, and use. The first step is a safe sign-up and login process in which the user enters their name, phone number, email address, and password to create an account. The user additionally provides an email address and password to prove who they are. When a user logs in to their own personalized user interface (UI), they are directed through the process of registering their location step by step. First, they choose the state, and then they click on it to choose the district for their profile and to provide a “Hospitals Near Me” option. After establishing the location, the user is taken to an AI-powered Symptom Chatbot that takes the symptoms as input and forecasts likely diseases. This is done utilizing integrated machine learning and AI APIs like Google Gemini. The chatbot is well thought out, and it goes a step further by showing the results in clickable bubbles, like precautions and medicines. This makes it easier for the user to find safety tips or typical medical advice. To help users get expert medical treatment when they need it, the app also gives them a list of nearby hospitals with their names, phone numbers, and addresses. The goal of the system is to be an early medical guidance app, not a replacement for doctors. Its goal is to help people figure out any probable health problems, any possible delays in taking action, and the best ways to get to the hospital when required. The idea is like a digital health companion that helps people take charge of their health and find their way through health problems if they need to. The healthcare system has evolved because of AI, deep learning, and machine learning. These technologies make it easier to diagnose, predict, and keep an eye on patients. In healthcare, the most frequent models are convolutional neural networks (CNNs), support vector machines, transformers, and recurrent neural networks (RNNs). They aid with genomes, medical imaging, and predicting diseases. The CNN is more accurate when it comes to medical imaging. Long and Short-term Memory and RNN look at a lot of organized and unstructured data to detect chronic diseases. Transformers assist in processing a lot of genomic datasets and clinical texts to improve tailored treatment. The model looks at how the disease spreads before the symptoms get worse. It improves patient outcomes, operational efficiency, and access to healthcare, which is excellent for both patients and healthcare systems. New technologies like explainable AI and federated learning could revolutionize the healthcare system in the future to make it easier for doctors to understand, be honest about, and trust the choices they make.