PREDICTIVE AND RECOMMENDATION BASED HOLISTIC HEALTH CARE SYSTEM USING MACHINE LEARNING
"Healify Me" is a user-friendly healthcare remedy platform that merges traditional healing wisdom with modern technology to offer personalized and accessible wellness solutions. Built using Flask, HTML, CSS, and Python-based machine learning, the app is designed for people who want natural yet trustworthy ways to care for their health. In a space where most health apps feel either too generic or too fragmented, Healify Me stands out by combining remedies from Ayurveda, Siddha, home practices, and modern medicine—all in one simple and organized platform. What makes it unique is its interactive body layout: users can click on body parts, describe symptoms, and get real-time health suggestions powered by machine learning models. Instead of depending on live consultations, the app offers predictions, dietary guidance, and remedy suggestions so users can manage their health at their own pace. With an easy-to-navigate interface, even first-time users can quickly understand and explore remedies based on gender, symptoms, and specific health conditions. A structured backend ensures secure data storage and fast response times, while administrators can manage remedies and categories through a built-in control panel. The system also supports dynamic updates, allowing new remedies and categories to be added without hassle. By blending age-old healing traditions with current digital tools, Healify Me encourages self-care, prevention, and better daily wellness. It doesn’t just act as a remedy guide—it becomes a trusted digital partner in the user’s health journey, empowering individuals with reliable, context-aware options. In today’s fast-paced world, this thoughtful and integrative approach offers a meaningful shift toward personalized, informed, and holistic healthcare for everyone.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-08
- DOI
- https://doi.org/10.5281/zenodo.15362305
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00