Intelligent Machine Learning–Based Models for Preventive Healthcare and Personalized Health Promotion

Abstract

These days, conventional medical & healthcare system procedures are being altered by new, highly developed technologies. New technologies based on improved data access, deep learning, AI, big data, the cloud, and other machine learning techniques include rising mobile health (M-Health) systems. Early risk identification, ongoing monitoring, and customised therapies that take individual differences in genetics, lifestyle, & environmental factors into account are necessary for preventive healthcare and personalised health promotion. Through statistical analysis and data-driven decision making, this study offers an intelligent machine learning-based platform intended to enable personalised health promotion and preventative healthcare. To create reliable predictive and classification models, the suggested method combines a variety of health data sources, such as wearable sensor data, electronic health records, and lifestyle data. To identify disease risk patterns, predict health outcomes, and suggest individualised preventive measures, advanced machine learning techniques like supervised learning, model ensembles, and deep neural architecture are used. By facilitating proactive and customised health interventions, the model prioritises early disease prediction, lower healthcare costs, and more patient participation. The suggested ML-based models show good accuracy, scalability, and flexibility across a variety of demographic groupings, according to experimental evaluations. The results demonstrate how sophisticated machine learning systems have the ability to improve population wellness and standard of life by converting traditional reactive medical care into a proactive, individualised, and preventative healthcare paradigm.