Abstract: The Ambulance Pulse Risk Prediction system proposes an AI-driven proactive emergency response solution for hospitals, aiming to revolutionize traditional reactive measures by predicting medical emergencies before they occur. Leveraging real-time data from diverse sources including patient health records, wearable devices, and environmental factors, advanced machine learning algorithms analyze patterns and correlations to identify heightened risks such as cardiac events and strokes. Through proactive alerting, hospitals can allocate resources more efficiently and intervene preemptively, potentially preventing emergencies and improving patient outcomes. Key components encompass data gathering, algorithm development, system integration, and validation with a strong emphasis on privacy and ethical considerations. By harnessing the power of AI, this system has the potential to transform emergency medical services, enhancing patient care and saving lives on a global scale.
Keywords: Pulse sensor, Artificial intelligence, IoT, Ambulance Pulse Risk Prediction, CatBoost Algorithm.
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DOI:
10.17148/IJARCCE.2024.13481
