Hyrbid Random Forest Infused CNN for Huntington Disease Classification from Genomic Variant Data
Keywords:
Huntington’s Disease, Genomic Variant Classification, Hybrid CNN-Random Forest, Deep Learning, Machine Learning, Predictive Genomics, Feature Importance, Precision Medicine.Abstract
Huntington’s Disease (HD) is a progressive neurodegenerative disorder caused by CAG trinucleotiderepeat expansions in the HTT gene, affecting 10.6–13.7 per 100,000 individuals worldwide, with typicalonset between 30 and 50 years. Traditional computational approaches for HD classification achievelimited accuracy (78–85%) due to noisy variant features and population heterogeneity. To overcome these challenges, this study proposes
References
Bishop, Jerry E.; Waldholz, Michael (1990). Genome: The Story of the Most Astonishing Scientific Adventure of Our Time – the Attempt to Map All the Genes in the Human Body. New York: Simon and Schuster. p. 201.
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Published
2025-05-25
How to Cite
A. Sravan, D. Varshini, T. Shravani, P. Akshay, A. Umesh Chandrakanth Reddy. (2025). Hyrbid Random Forest Infused CNN for Huntington Disease Classification from Genomic Variant Data. Journal of Computational Analysis and Applications (JoCAAA), 34(5), 389–397. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4618
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