The Role of Data Stream Mining in Improving Self-Medication Practices in the Syrian Arab Republic

Abstract

Self-medication is a widespread practice throughout the world, especially in the Syrian Arab Republic. However, if done incorrectly, this method might be harmful. Data stream mining, which involves analyzing vast amounts of data from multiple sources, has proven to be an effective technique for enhancing self-medication practices. The role of data stream mining, and how it may evaluate information from various sources like social media, electronic health records, pharmacy sales data, sensor-based medical devices, etc. have been explored for the research. Healthcare professionals in Syria can improve patient outcomes and safety related to self medication by employing data stream mining to uncover important information about patient behaviors, drug effectiveness, and adverse events. In this paper, a strategy for generating evidence-based medical data utilizing data stream mining techniques is suggested. Here, methods including association rule mining, categorization, and data clustering have been described. For future progression of this research, the information gap and other data collection-related problems need to be resolved. The implementation demonstrates that healthcare professionals can use a variety of data stream mining techniques to better understand drug usage patterns and spot opportunities while also learning about the prevalence of self-medication in different parts of the Syrian Arab Republic.