AI-Driven Big Data Analytics Framework for Real-Time Healthcare Insights
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P108Keywords:
AI in healthcare, big data analytics, real-time insights, predictive analytics, descriptive analytics, prescriptive analytics, healthcare data pipeline, machine learning, deep learning, clinical decision support, Internet of Medical Things (IoMT), personalized medicine, cloud-based healthcare, streaming data processing, explainable AI (XAI)Abstract
Wearable sensors, electronic health records (EHRs), medical imaging, and devices that are connected to the Internet of Things (IoT) are all helping to gather more data about the healthcare sector than ever before. Real-time analytics is very important for improving the quality of treatment for patients and making sure that operations run more smoothly. Because they rely on batch processing and data models that aren't flexible, traditional analytics methods don't always deliver useful and timely insights. The reason is that the data is slow and they can't handle large, fast, and varied data streams. Hence, this limitation makes it difficult to make decisions, it wastes the opportunity of early diagnosis, and it also wastes resources. This research, which is a huge data analytics platform that is run by AI and allows getting real-time healthcare insights to solve those problems, is given to us. By integrating innovative AI algorithms such as machine learning (ML), deep learning (DL), and natural language processing (NLP) with distributed big data platforms, e.g., Apache Spark, Hadoop, and cloud-native infrastructures, the framework facilitates large-scale descriptive, predictive, and prescriptive analytics exploration. The system has the ability to process both structured and unstructured data in a wide range quickly and accurately. The use of scalable cloud computing infrastructures, streaming analytics systems like Apache Kafka and Amazon Kinesis, and fast data intake pipelines achieves this. AI-driven anomaly detection allows medical staff to monitor their patients closely in real time. They can also apply strong machine learning algorithms to speculate what could happen in the future. Adaptive patient profiles give the opportunity to deliver care that is personalized to each patient. Also, deploying explainable artificial intelligence (XAI) techniques assures that the prediction models are unambiguous, understandable, and dependable
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