AI-Powered Healthcare Tracker Development: Advancing Real-Time Patient Monitoring and Predictive Analytics Through Data-Driven Intelligence"

Abstract

The rapid progress in artificial intelligence (AI) and data analytics has revolutionized healthcare and made predictive insights possible that enhance clinical decision-making by means of real-time patient monitoring.   This paper describes the development of an artificial intelligence-driven healthcare tracker meant to collect, evaluate, and interpret patient data for proactive medical control.   The proposed system effectively forecasts future health dangers, monitors key health indicators, and uses big data analytics and machine learning algorithms to enhance early disease identification.   The main purposes of the tracker are feature engineering, automated data purification, and augmentation, therefore ensuring the dependability and robustness of healthcare data sets.   Combining wearable sensors, electronic health records (EHRs), and cloud computing, the system offers customized recommendations and real-time health status updates.   Furthermore, utilized to identify patterns in patient data are predictive modeling techniques, therefore supporting early intervention and preventive therapy projects.   Comparatively to traditional monitoring systems, experimental results show that the AI-driven healthcare tracker greatly increases diagnosis accuracy, patient participation, and clinical efficiency.   Using many machine learning metrics, the model's performance is evaluated and shows notable progress in anomaly detection, disease prediction, and customized healthcare recommendations.   The proposed approach might change remote patient monitoring, lower hospital readmissions, and improve healthcare resource economy.   This study emphasizes the transforming power of artificial intelligence and data analytics in healthcare, therefore enabling the creation of more smart, flexible, and data-driven healthcare solutions.   Later studies will focus on enhancing the functionality of the system by means of deep learning models and real-time artificial intelligence decision support systems thus raising prediction accuracy and patient outcomes.