Enhancing Accessibility in Health Care: Voice and Data Controlled Medical Assistant Especially Abled Persons

Abstract

Healthcare accessibility remains a significant challenge for specially abled individuals who face barriers in communicating symptoms and accessing timely medical support. This research introduces a novel solution a Voice and Data-Controlled Medical Assistant powered by machine learning aimed at improving healthcare access for specially abled persons. The assistant takes input in both voice and text formats, analyzes the provided symptoms using a trained model, and outputs predicted diseases along with detailed symptom descriptions, preventive measures, medication recommendations, suitable workouts, and diet plans. This system not only bridges the communication gap but also promotes self-reliance in managing health.