Handling Spare data in secure health records from leakages using Dragon Chain method comparing with Random Forest algorithm

The main theme of this project is to find leakages of data, handle the data leakages and enhance time complexity using Dragon Chain compared with Random Forest. The study contains two groups i.e, Random Forest is developed in the first group containing 200 samples and Random Forest in the group 2 with 200 samples where G power value=0.8. The performance of accuracy is improved in Dragon Chain which has accuracy of 92.12% and Random Forest accuracy is 85% with the power value of 80%. From an independent sample
$T$
test the mean value is (0.665), mean accuracy detection
$(\pm 2\ {\text{SD}})$
and the significance value is 0.486
$(\mathrm{p}i0.05)$
. DragonChain has better accuracy of 92.12 percent compared to Random Forest algorithm.