PREDICTING PATIENT FALLS IN HOSPITALS USING SENSOR DATA AND MACHINE LEARNING

Abstract

Abstract:- This exploration paper investigates the operation of detector data and machine literacy ways to prognosticate patient cascade in sanitarium settings. Case falls pose a significant threat to healthcare providers and can result in severe injuries and complications for cases. Using detector data collected from colourful sources within sanitarium surroundings, including wearable bias and environmental detectors, this study aims to develop a prophetic model able to relate patterns and threat factors associated with case falls. By employing machine literacy algorithms similar to decision trees, support vector machines, and neural networks, the proposed approach analyses temporal and spatial patterns in detector data to read the liability of case falls. The findings of this exploration contribute to advancing patient safety enterprise in healthcare settings and give precious perceptivity for designing visionary interventions to help cascade and alleviate associated pitfalls.