Open Health: A Comprehensive AI Tool for Remote Healthcare for Multi-Diseases
TL;DR
This comprehensive project focuses on Multi-Disease Detection, employing a diverse set of algorithms, encompassing deep learning, standard machine learning, transfer learning, and hybrid models such as VGG-19, ResNet50, Random Forest, and Gradient Boosting.
Abstract
The abstract in an era where healthcare demands precision, and personalized solutions, “OpenHealth” emerges as a ground breaking accessibility initiative at the intersection of technology and medicine. This comprehensive project focuses on Multi-Disease Detection, employing a diverse set of algorithms, encompassing deep learning, standard machine learning, transfer learning, and hybrid models such as VGG-19, ResNet50, Random Forest (RF), and Gradient Boosting. Diseases across specific organs, such as the brain, kidney, heart, liver, and lungs, are accurately predicted, and model performance is rigorously assessed through metrics like accuracy, recall. Multi-disease detection enables simultaneous identification of multiple conditions, reducing diagnostic time and supporting early detection of comorbidities in clinical decision-making scenarios. Adding a layer, “OpenHealth” integrates with large language models from the Open-source libraries like Hugging Face, providing personalized information based on individual health profiles. Likewise, the design extends its impact by incorporating an AI dietitian and food recommender, acclimatizing salutary recommendations to individual health conditions. scrupulous association is assured through devoted directory structures, fostering a modular and justifiable frame. Leveraging Machine Learning Operations (MLOps) like Dockers, DVC, Evidently, and MLflow enhances the overall efficiency and reliability of healthcare systems.