Real-Time Disease Prediction Using Big Data and Human-Computer Interaction

Big data streaming involves managing the vast volumes of data generated continuously by wearable medical devices with sensors, healthcare cloud platforms, and mobile applications. Traditional methods for processing this data are often time- and resource-intensive. To address this challenge, there is a need for efficient and scalable real-time big data stream processing. This study introduces a novel architecture for a big data-driven real-time health status prediction and analytics system. In this architecture, we replace Hadoop MapReduce with Spark to enable a parallel, distributed, and scalable decision tree algorithm capable of handling real-time computations. This model is then applied to streaming data from various sources, supporting the prediction of health statuses across multiple diseases. Using distributed streaming data, the system predicts health conditions associated with different disorders. To evaluate the performance, we compare Spark's decision tree (Spark DT) with traditional machine learning tools such as Weka. Key performance metrics, including execution time and throughput, are analyzed to assess the effectiveness of the proposed architecture. Experimental results demonstrate that the proposed system can effectively manage and predict vast amounts of real-time IoT-enabled medical data related to various disorders, showcasing its potential for real-time healthcare applications.