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@article{194974,
        author = {Bharathnath Chowdary Konanki and Chintalapati Sarath Chandra and Gummidipudi Somesh and Mettukuru Venkata Sai Nikhilesh Reddy and Ms. Mary Selvan},
        title = {Predicting Medical Equipment Failure Using Machine Learning (Stream Processing)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6263-6272},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194974},
        abstract = {Predictive maintenance of medical equipment is critical in modern healthcare environments, where unexpected device failures can lead to severe consequences, including delayed treatments, increased operational costs, and potential risks to patient safety. Hospitals rely on a wide range of complex devices such as infusion pumps, ventilators, and monitoring systems, which require continuous monitoring to ensure reliability and optimal performance. Traditional maintenance strategies, which are either reactive or based on periodic inspections, often fail to provide timely detection of potential failures. To address these limitations, this study proposes a real-time predictive maintenance framework for medical equipment using IoT-based data streaming and machine learning techniques.
The proposed system integrates simulated sensor data generation with a real-time stream processing pipeline to continuously monitor device health parameters such as temperature, vibration, pressure, runtime hours, and error logs. Data is transmitted using the MQTT protocol through a cloud-based broker, enabling efficient and scalable communication between devices and processing components. Apache Spark is utilized for real-time stream processing, where incoming data is structured, preprocessed, and analyzed dynamically. The system employs an XGBoost-based machine learning model to predict equipment failure risk by capturing complex relationships between multiple operational features. Each device is categorized into three risk levels—low, medium, and high—allowing for prioritized maintenance actions.
To ensure scalability and persistence, prediction results are stored in cloud storage using Azure Blob Storage, while a Gradio-based web interface provides real-time visualization and monitoring capabilities for users. The architecture incorporates parallel processing and asynchronous communication to maintain low latency and high throughput in continuous data streams. Experimental evaluation demonstrates that the proposed system can effectively identify potential equipment failures in advance, enabling proactive maintenance strategies. The results indicate that integrating stream processing with machine learning offers a robust and scalable solution for intelligent healthcare equipment monitoring and failure prediction.},
        keywords = {Predictive maintenance, medical equipment failure prediction, real-time stream processing, IoT-based monitoring, Apache Spark streaming, MQTT communication, XGBoost, machine learning, healthcare analytics, anomaly detection.},
        month = {March},
        }