A Novel Dolph-Chebyshev Filter for Prediction of Huntington’s Disease

Mutation in all the cases doesn’t specify cancer; rather a special type of mutation, where a trinucleotide repeat (cytosine-adenine-guanine (CAG)) in DNA exceeds the normal range, causes Huntington’s disease (HD); a mutational disorder. The present work specifies a non-invasive method of spectral estimation technique based on Trigonometric mapping. A Fourier Transform approach along with the Dolph-Chebyshev filter has been designed and applied to raw genomic data for predicting the disease. Moreover, the proposed algorithm has been tested for both healthy and HD benchmark datasets. Since a disease gene is more random compared to a healthy one, the variance is calculated. It is observed that in the case of all HD datasets, the value of $\operatorname{SNR}(\mu / \sigma)$ is less than 0.1, whereas, in the case of a healthy gene set, it is found to be more than 0.1. For all other parameters also the result is found to be satisfactory.