Prediction of cardio vascular disease using block chain technology

Abstract

The personal data consisting of various historical and vital parameters leading to cardio vascular disease is often very poorly understood. The main reason for such a scenario in a data driven developing country is mainly due to fact of lack of understanding importance of the data and numerous independent stake holders contributing to the health care of the country. It is accepted by the world health organization that some of the developing countries are yet to reach a phase of considerable data repository that could analyzed to establish the major parameters in the developing countries. India not far behind in this observation has not been able to create a repository of individuals that could be used for analysis and prediction of any disease for that matter. This research proposes blockchain technology to properly document the historical data that would lead to quality of health, analysis of health in the given region and prediction in case of environmental and epidemics. The blockchain helps in creation of distributed ledger personal record which is secure due to the fact that patient is in control of the data and could monitor the privileges and understanding of views allowed, This leads to data driven health care with the industry and other stake holders could request the data in partial or full to analyze the trends and community requirements. This could also be a potential source of earning for a patient for leasing data to pharmaceutical companies for research and development of the industry. It is observed that the factors leading to CVD change from region to region and some of the significant factors do not form a minimal set of factors in other region. This led to generalization of method to predict the minimum factors of CVD. Machine learning methods available till date have shown good results in some region and are useless in others. This leads to a method of identifying a set of best fit methods for a region. Research is conducted to consider the best fit methods for the region. A hybrid frame work