Scalable Data Clustering For Health Behavior Analysis In Big Data Cloud Environments

This work addresses the requirement for scalable data organization methods in big data cloud systems for behavioral analysis. Researchers embrace an interpretive attitude and use a deductive strategy and descriptive method to find subtle patterns in the data on health behavior. It makes use of sophisticated clustering algorithms that are tailored for based on the cloud processing and leverages additional data from numerous sources. Four unique behavioral clusters can be identified based on the results: “Active Lifestyle Choices Enthusiasts,” “Balanced Wellness Seekers,” “Sedentary nevertheless Conscious Eaters,” in addition “Erratic Healthcare Engagers.” These clusters include various levels of physical activity, nutritional preferences, and nights of sleep and offer useful information for customized therapies. The effectiveness of interpretivism in comprehending subjective sensations and the use of cyberspace for effective data processing are highlighted in critical examination. However, suggestions for more research include exploratory methods, longitudinal evaluation of data, and the incorporation of immediate information streams. This study advances the field by showing how scalable clustering algorithms in cloud systems may be used to analyze behavior related to health. A thorough framework for comprehending behavioral patterns is provided by the integration of interpretative concepts, which enhances the depth of investigation. This strategy has the potential to inform tailored healthcare plans and targeted actions, ultimately benefiting public health programs.