AI-Driven DNA Insights and Public Health Data Integration for Enhancing Personal Healthcare in Critical Disease Prevention

Abstract

The approach taken in this paper tries to combine personal health and preventive measures of chronic illness with predictive machine learning. The research develops a personalized medicine with the help of the public health and DNA dataset to construct predictive machine learning algorithms. This experimental method will develop many tailor-made public health plans. This focused strategy has possibilities and approaches that are discussed in the paper. The health sector and the population will be impacted positively since predictive data on public health machine learning will significantly alleviate the millions of people who are diseased and other ailments. It will enable the health professionals to compete and control their resources and patients. Machine learning combined with DNA datasets can be highly effective to create customized health approaches to the populace. This more focused strategy will result in less chronic and other health problems and disease burden on the public sector. The sector public health patients and the health professionals will be empowered in this paper. Public health machine learning will contribute to resource load of health professionals and make them able to handle their patients. A combination of machine learning and publicly available DNA data will be successful in the customization of primary health strategies. The present paper is devoted to these issues and the efficient public health DNA data sets. Out of these strategies, prevention of chronic diseases and the health of the great populace will be radically controlled.

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