A Novel Hybrid Machine Learning and Generative AI Framework for Personalized Healthcare Analytics

In recent times, the percentage addition of healthcaresystems in the form of accessible, reliable, and vape-helpful hasnecessitated artificial intelligence( AI) to be used in complaintsvaticination and health recommendation creation. The paperproposes the application of smart DSS-based health Vaticinationsystem incorporating rule-based information that equals, ensemblemachine literacy/data and generative artificial intelligence to makecorrect complaint vaticination and health advice that is substantive.The suggested system enables the druggies to provide demographicinformation, including: age, coitus, physical feature, including;height and weight, and a pre-determined set of seven symptoms.This method of providing precise prognostications of specificcomplaint records was initially applied using an exact dataset thatoffered genuine prognostications of high quality of a large scalemedical dataset, icing maximum delicacy for exactprognostications. In the case of scripts having no exact match,enhanced ensemble literacy style that joins supervised machineliteracy classifier and a soft voting calculation method is used todetermine precise prognostications. point encoding andnormalization methods are also employed to uphold thickness inthe model. the model also calculates the Body Mass Index( BMI) togrease the health analysis. To provide the model that is easier tounderstand and interact with, a generative AI module is used toprovide the case-friendly health recommendations, such asprecautions, doses, and do nots, along with the general healthrecommendations, which are based on the predicted illness. IndexTerms Disease Prediction, Generative Artificial Intelligence,Ensemble Machine Learning, Personalized Healthcare, DigitalHealth Assistant.