Integrative approach for modern health risk modeling and predicting in patients through artificial intelligence method

Abstract

This research suggests a modeling approach for health risk prediction that utilizes an ambient environment and Artificial Intelligence (AI). The proposed AI-based Health Risk Modeling and Predicting System (AI-HRMPS) included gathering medical records from chronic illness patients, including Electronic Health Records (EHR), Personal Health Records (PHR), medical records, and environmental variables from a health portal. Diverse data is combined via choosing, cleaning, modeling, and assessing raw data, followed by data production. Sensor data is standardized by converting the time-domain details to frequency-domain details. The standardized input is processed using an AI to provide an ambient environment. A health risk prediction system has been proposed to analyze specific health issues about environmental factors. The program utilizes ambient context patterns identified via metadata and AI. The risk prediction framework is integrated into a person's risk alert/prevention mechanism. The system might substantially influence healthcare and AI studies, ultimately enhancing the future society's standard of life.