Summary of Mltogai: Semantic Web Based with Machine Learning For Enhanced Disease Prediction and Personalized Recommendations Using Generative Ai, by Shyam Dongre et al.
MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease Prediction and Personalized Recommendations using Generative AI
by Shyam Dongre, Ritesh Chandra, Sonali Agarwal
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MLtoGAI system integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and provide user-friendly explanations through ChatGPT. The system consists of three key components: a reusable disease ontology, a diagnostic classification model, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT. This approach improves prediction accuracy and ensures results that are easy to understand. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that recommendations are relevant and easily understood by individual patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new system called MLtoGAI, which helps doctors make better predictions about diseases and gives people clear advice on how to take care of themselves. The system uses three main parts: a big database of information about different diseases, a tool that can diagnose what’s wrong with someone based on their symptoms, and a way to explain the results in simple language using ChatGPT. |
Keywords
» Artificial intelligence » Classification » Machine learning
