Hybrid Machine Learning For IoT-Driven Heart Health Prediction

Abstract: The rapid growth of intelligent healthcare technologies has enabled continuous monitoring of human health through connected devices and smart sensors. Heart-related illnesses require immediate attention and timely analysis because delayed diagnosis may lead to severe complications. This research presents a smart IoT-based framework designed for early heart risk identification using adaptive learning and sensor-driven analytics. The system acquires real-time physiological information through ECG, pulse oximeter, and temperature sensors connected to an embedded microcontroller platform. Unlike traditional healthcare prediction systems that depend completely on pre-labeled datasets, the proposed framework introduces an adaptive learning strategy capable of dynamically categorizing incoming health data patterns. Processed sensor readings are analyzed using Artificial Neural Network (ANN) and Random Forest techniques to determine the probability of abnormal heart conditions. The proposed approach improves prediction consistency, supports continuous monitoring, and minimizes dependency on cloud-based infrastructure. Experimental evaluation demonstrates that the framework provides reliable prediction performance with improved adaptability for real-time healthcare environments.

Keywords: Smart Healthcare, IoT Sensors, Heart Risk Detection, Artificial Neural Network, Random Forest, Real-Time Monitoring, Intelligent Healthcare