An Integrated Machine Learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment

This research suggests a comprehensive healthcare system that successfully blends machine learning (ML) and the internet of things (IoT) in order to increase healthcare efficiency and provide customized treatment in emergency ward situations. We employ a range of sensors, including those that detect pressure, temperature, oxygen levels, and heart rate, to continuously collect real-time patient data. In the outcomes of our trials, the ML models (SVM, ANN, RF, and DT) displayed varying degrees of accuracy, precision, recall, and F1 score. The most effective model, ANN, obtained an astounding accuracy rate of 92%. SVM came in second with an accuracy of 89%, followed by RF and DT in third and fourth, respectively, with 86% and 84% accuracy. Real-time data and predictive analytics are now available to healthcare professionals, which has a significant beneficial influence on efficiency and enables the customization of patient treatment plans. The number of emergencies was dramatically reduced as a result of these foresights, which improved patient outcomes. This study shows the potential for a patient-centered, technology-driven healthcare system that elevates the bar for healthcare delivery. The study results not only demonstrate the viability of our approach but also point out significant advancements in medical technology, paving the way for more effective, flexible, and patient-focused healthcare systems.