International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Health Diagnosis from Symptoms |
|---|---|
| ABSTRACT | In recent years, early detection of diseases has become very important for improving healthcare services. Many people ignore initial symptoms due to lack of medical awareness, which may lead to serious health issues. This paper presents a symptom-based health diagnosis system using machine learning techniques. The proposed system allows users to enter their symptoms through a web-based interface and predicts the possible disease based on trained machine learning models. The system is developed using Python and machine learning algorithms to analyze symptom patterns and provide accurate predictions. This approach helps users to get a basic understanding of their health condition at an early stage. The experimental results show that the proposed system provides effective and reliable disease prediction and can be helpful as a preliminary health assistance tool. |
| AUTHOR | SANDHYA DHAKAD, SURBHI BOHRE, AMAN KUMAR, SANJU KUMARI, PROF. SAPNA RAIKWAR B.Tech Student, Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Oriental Institute of Science and Technology, Bhopal, India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Oriental Institute of Science and Technology, Bhopal, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312135 |
| pdf/135_Health Diagnosis from Symptoms.pdf | |
| KEYWORDS | |
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