Andere Kunden interessierten sich auch für
![Foundations of IoT in Healthcare]( "Foundations of IoT in Healthcare")
Foundations of IoT in Healthcare29,99 €![Blockchain Integration in IoT-based Smart Healthcare Systems]( "Blockchain Integration in IoT-based Smart Healthcare Systems")
Blockchain Integration in IoT-based Smart Healthcare Systems29,99 €![Optimized and Generic Multi-Party Data Communication Framework in IOT]( "Optimized and Generic Multi-Party Data Communication Framework in IOT")
Optimized and Generic Multi-Party Data Communication Framework in IOT40,99 €![Smart Healthcare Systems and its Applications: A Research Perspective]( "Smart Healthcare Systems and its Applications: A Research Perspective")
Smart Healthcare Systems and its Applications: A Research Perspective29,99 €![Architecting Fog-Cloud Networks: Principles,Technologies, and Apps]( "Architecting Fog-Cloud Networks: Principles,Technologies, and Apps")
Architecting Fog-Cloud Networks: Principles,Technologies, and Apps29,99 €![An Integrated Communication System for a Special Needs Children Center]( "An Integrated Communication System for a Special Needs Children Center")
An Integrated Communication System for a Special Needs Children Center33,99 €![Leveraging IoT, SDN, and AI Innovations for Smart Systems]( "Leveraging IoT, SDN, and AI Innovations for Smart Systems")
Leveraging IoT, SDN, and AI Innovations for Smart Systems90,99 €
The book "Smart Healthcare Systems: Reducing False Predictions of Chronic Diseases Using IoT and Machine Learning" presents an innovative, cost-effective IoT-based Ambulatory Blood Pressure Monitoring (IABPM) system aimed at real-time tracking and early detection of Chronic Heart Disease (CHD). It leverages wearable sensors and cloud integration to record blood pressure and pulse data throughout the day, enhancing patient mobility and care. The system applies ML algorithms like Naïve Bayes, KNN, Decision Tree, SVM, and XGBoost to clinical datasets (SMHRCE and Kaggle) to predict Early Warning Scores (EWS). The proposed model shows high accuracy (up to 99.85%) and helps reduce healthcare costs, support proactive intervention, and improve diagnostic precision. The project bridges gaps in traditional ABPM systems by offering continuous monitoring, automated alerts, and cloud accessibility-paving the way for improved chronic disease management through technology integration.