The intensifying cost of healthcare needs tools for up-to-date insurance ranges. Machine Learning approaches for predicting individual healthcare insurance costs are analyzed with the help of patient records, and a personalized cost estimation model empowers individuals, particularly in rural areas, to navigate complex insurance options. Unlike existing solutions, our model does not predict specific company costs but provides a personalized cost range. To overcome to proposed this paper focuses on affordability and informed decision-making and addresses challenges like limited health literacy and lack of awareness of government-provided schemes. The machine learning algorithms are Gradient Boosting and Random Forest to achieve high accuracy enabling all individuals, especially those in underserved communities, to make informed healthcare investment decisions.

Blockchain expertise in arrears the secure dealings of cryptocurrencies similar to bitcoin can construct it easy to make transparent meta-analysis where reproducibility is raising concern. With the facility to form time-stamped and tamper-proof, records of measures that can reinforce the simplicity and reproducibility of meta-analysis. In this chapter, to construct a fog/cloud computing (CC) mechanism that should frequently record and track analysts’ decisions like how studies are gathered and analyzed as part of a meta-analysis. In case, where meta-analysis current unpredictable outcomes. The blockchain can aid locate precisely how they chanced based on individual timestamps of logical decisions. The suggested mechanism would faultlessly form trustworthy patient clinical trial demographic profile documents without adding the difficult process of conducting a meta-analysis with the help of supervisor learning. The experimental result is on security based cloud access demographic clinical trial patient profiles from the cloud environment for classification purposes.