Intelligent Patient Health Monitoring and Predictive Analysis Using Machine Learning Techniques

Abstract

With the increasing complexity of healthcare, particularly in managing patients with chronic diseases or critical conditions, real-time monitoring has become essential. Traditional methods, which rely on periodic checks and manual assessments, are often insufficient for detecting rapid changes in a patient’s condition. This paper presents a comprehensive solution that integrates Internet of Things (IoT) technology with machine learning (ML) to offer continuous health monitoring and predictive analysis. IoT-enabled wearable sensors collect real-time data, such as heart rate, body temperature, and oxygen saturation (SpO2), which are then analyzed by ML algorithms to predict health trends and identify risks early. By automating data collection and analysis, the system provides healthcare providers timely alerts, allowing prompt intervention when necessary. The proposed system employs machine learning models like Random Forests, Logistic Regression, and Support Vector Machines (SVM) and found that the Random Forest classifier provides the highest prediction accuracy at 92%. This allows for better resource allocation in hospitals and ensures that patients receive the care they need before their condition deteriorates. Moreover, the system’s remote monitoring capabilities make it ideal for home care, significantly reducing hospital readmissions. This integration of IoT and ML aims to enhance healthcare delivery, improve patient outcomes, and reduce the workload on healthcare professionals by automating critical tasks.