Early Detection of Pediatric Cardiomyopathy Disease Using Window Based Correlation Method from Gene Micro Array Data

Disease prediction through gene is a challenging task. Researchers have proposed algorithms to identify disease from genes. Traditional algorithms prioritize through annotation and combines the structures in biological process or molecular functions and compared with annotations of known disease genes for classification. Pediatric Cardiomyopathy is a disease due to disorder in heart muscle and identification at early stage is a challenging problem. In this paper, the above problem solves through Window Based Correlation (WBC). In WBC, Global data is reduced to spatial data using block reduction technique. After Data reduction, strong relationship analysis between the genes is identified through RMSE values between the genes. This RMSE values helps to detect the pediatric cardiomyopathy at early stage using Window based correlation method. From the results, ablation study proves an accuracy of prediction is about 85%.