A Decision Support System for Health Management: Integrating Big Data and Machine Learning in Information Systems

Abstract

Healthcare tools are changing quickly, and the amount of medical data is growing. This has made health management more difficult. Decision Support Systems (DSS) are important tools that can help with these problems by giving doctors smart advice to help them make smart choices. Combining Big Data analytics and Machine Learning (ML) methods in a Decision Support System is what this study suggests as a way to handle health better. By using big data sets like electronic health records (EHRs), patient tracking systems, genetic data, and other health-related data, the goal is to improve the quality and efficiency of healthcare services. The suggested DSS structure is based on three main parts: combining data, handling it analytically, and making choices. First, data integration is all about bringing together different sources of data, making sure the data is correct, and building a single platform that makes it easy to find the information you need. Big Data tools, like Hadoop and Spark, are used to deal with huge amounts of organized and random data at the same time. Machine learning models, especially controlled and unstructured learning algorithms, are used to get useful information from these datasets. For example, they can find trends, guess how diseases will progress, and suggest individual treatment plans. Second, the system's analytical processing layer uses advanced machine learning methods like classification, regression, and grouping to help with decision-making and predictive analytics. These models are trained on a lot of healthcare data, which gives doctors useful information about how diseases grow, what causes them, and the best ways to treat them. The system is always learning, which means that models can be improved based on new data. This makes the system more accurate and flexible.