Leveraging Big Data and AI for Personalized Healthcare Solutions in Smart Cities

Abstract

The emergence of smart cities brings with it new challenges and opportunities regarding personalized healthcare services. The prevalence of chronic diseases, an aging population, and a rising need for efficient health management have put additional demands on healthcare to seek innovative solutions. This study discusses big data and AI integration to provide personalized healthcare services in smart cities. A quantitative research approach was implemented based on data from wearable devices, environmental sensors, and healthcare records. Health data analytics utilized machine learning algorithms, including decision trees and neural networks, to construct predictive models in the management of chronic diseases and early warning systems. Preprocessing and analysis of data were done using Python, R, and Hadoop to ensure accuracy and scalability. The AI models achieved an accuracy of 85% in the identification of early symptoms of chronic diseases like diabetes and cardiovascular conditions. A 20% increase in citizen satisfaction concerning healthcare services was noticed in those regions that implemented this model of AI-driven health management. Besides this, proactive alerts generated by the model resulted in a measurable decrease in the rate of progression of diseases, particularly in elderly populations. The results prove that AI can bring transformational changes to personalized healthcare in smart cities, in line with global research trends. These findings are academically significant in offering new methods of data analysis and are of practical value for policymakers in the design of efficient health management strategies. Limitations include regional bias and data privacy challenges. Future research should be directed toward a wider integration of data, advanced protection of privacy, and scalable solutions across cities to maximize the impact of AI-driven healthcare.