A Novel Future Predictive Approach to Model Healthcare Status Forecasting Based on BigData Evaluation Principle

The rapid advancement of technology and the exponential growth in healthcare data have necessitated the development of robust predictive models to anticipate future healthcare trends and outcomes. This paper introduces a novel predictive approach designed to model healthcare status forecasting using the Big Data Evaluation Principle. We aimed to predict the healthcare status of an individual using EHR and genomic data, as well as environmental lifestyle factors based on a comprehensive dataset by proposing novel approaches in this research. It looks to create complex machine learning models that can detect subtle patterns and forecast health outcomes - such as the onset, advancement or exacerbation of diseases. This system is a new one called BigData enabled Predictive Healthcare Modeling (BPHM) which has verified by cross-validation with the existing method Predictive Modelling for Healthcare Analyzer known as PMHA and cost of personal healthcare can be decreased; also quality of life can be improved using proposed systems. The accuracy of the proposed method is 97.8%. Referring to high-level computational methods, the proposed model is meant for resource allocation, treatment plan personalization and improved patient outcomes. Furthermore, the work will apply robust evaluation measures to assess performance of this model and investigate how it can support health policy-making as well decision making. This work ultimately aims to help build a patient-centric predictive, preventative and personalized healthcare in future.