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 | Intelligent Smart Health Monitoring System Using IOT and Machine Learning for Real-Time Disease Prediction |
|---|---|
| ABSTRACT | This paper presents an intelligent smart health monitoring system that integrates Internet of Things (IOT) sensors with machine learning algorithms for early disease prediction and real-time health analysis. The proposed system continuously collects both environmental and physiological data, including water quality parameters such as pH level and turbidity, as well as temperature, humidity, and user-reported symptoms like fever, fatigue, and nausea. The collected data is preprocessed, normalized, and analyzed using supervised machine learning models, including Random Forest, Decision Tree, and K-Nearest Neighbors (KNN), to identify patterns and predict potential health risks. The system was evaluated on a dataset of 300 samples using an 80:20 training and testing split. Experimental results show that the Random Forest model achieved the highest accuracy of 91.3%, outperforming other classifiers in terms of precision, recall, and F1-score. Additionally, the system provides real-time alerts and visual feedback through a user-friendly interface, enabling timely intervention and improved healthcare decision-making. The proposed solution is scalable, cost-effective, and particularly suitable for deployment in rural and resource-constrained environments where access to healthcare services is limited. Overall, this work demonstrates the effectiveness of combining IOT and machine learning for proactive health monitoring, early disease detection, and efficient risk management. |
| AUTHOR | NAYANA D H, KAVYA T S, PALLAVI D, N M PALLAVI, PROF. ARCHANA K N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404094 |
| pdf/94_Intelligent Smart Health Monitoring System Using IOT and Machine Learning for Real-Time Disease Prediction.pdf | |
| KEYWORDS | |
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