Real Time Patient Health Monitoring using IoT and Machine Learning Techniques

Smart healthcare systems have emerged as a trans formative solution to modern medical challenges by combining Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) technologies.This research proposes a Smart Healthcare Prediction System capable of continuously monitoring patient vital signs, storing them securely in a MySQL database hosted on XAMPP, and analyzing the data using advanced ML models in Python. The system collects physiological variables like blood pressure, heart rate, temperature, glucose levels, oxygen saturation, and respiratory rate. These values are processed using ML algorithms including Random Forest, Decision Tree, and Long Short-Term Memory (LSTM), enabling early prediction of diseases such as diabetes, hypertension, and cardiac disorders. The research demonstrates an efficient integration of IoT data acquisition, local database management, Architecture provides a scalable, cost-effective, and efficient way to monitor healthcare in hospitals, clinics, and smart homes. and predictive analytics, resulting in improved accuracy and rapid diagnosis. The proposed.