An Ideology for the Prediction of Critical Haplotype Blocks of Variants in Genes (Cyp2c9 And Vkorc1) for Warfarin (Anticoagulant) Drug Dosage to Treat Heart Patients Efficiently by Using Ml (Machine Learning) and Data Stream Mining Techniques

Now a day's on time treatment of heart diseases is a very critical part of medical diagnoses. So far there are total 50 SNP (Single Nucleotide Polymorphism) diagnosed that are responsible for the heart problems. But it is very hard to study all of the SNP together because of their different base pairs' locations or changes in base pairs positions (variations in genetic code A C G T). These all 50 SNP are present in all individuals with different variations, it is a tough job to calculate all the changes in this SNP set as there are total of (50^50) positions to calculate which is making it a huge data set. For acquiring a data set of all these positions, we will need some good Data Stream Mining (data mining techniques) to find out all the possible locations of all the variants responsible for the heart problems. In this research paper, we are giving a short analysis and introduction to the problem of heart patients drug dosage associated with anticoagulant (Warfarin) and its risks, solution for the challenge of calculating all variants of two genes (CYP2C9 and VKORC1) and advantages of the proposed solution in the future.