This work presents the development of a comprehensive healthcare app designed to improve early disease detection and enhance healthcare accessibility. The application integrates cutting-edge yet lightweight machine learning (ML) algorithms like Multinomial Naive Bayes and Decision Tree for symptom analysis and incorporates a range of innovative healthcare APIs like Edamam and Exercise API by Ninjas. Its primary objectives include empowering users with proactive health insights, facilitating timely medical assistance, and promoting overall well-being through personalised health recommendations. Key features of the app include accurate disease prediction through ML-driven symptom analysis, healthy recipe recommendations, customised exercise plans, and a conversational chatbot for diagnosis and treatment suggestions. By leveraging these functionalities, the app aims to enable users to take control of their health effectively, promoting paperless transactions via digital appointment and prescriptions. It also reduces physical visits to healthcare facilities, lowering carbon emissions associated with travel, which eventually paves the way to reduce environmental impact. The database integration via Firebase Auth offers data accessibility and security to data via services like encryption and Cloud Store. The intuitive navigation through the chatbot makes it approachable for users, including those who are less tech-savvy. Dark mode support aligns with sustainability goals by reducing eye strain and energy consumption. Thus, the work adheres to material design principles. With a user-centric approach, this app combines innovative ML-driven features and healthcare APIs to set a new standard in the digital health space, paving the way for advancements in early detection, personalised care, accessible healthcare services and long-term societal impact. © 2025 by the authors of this article.
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