Enhancing Genomic Disorder Prediction Through Feynman Concordance and Interpolated Nearest Centroid Techniques

Clinical biomedical applications of genomic technologies are extensive and provide possibilities to enhance healthcare covering the span of medical talents. Genome disorder prediction is an important issue in biomedical research. Genome disorders cause multivariate diseases such as cancer, dementia, diabetes, Leigh syndrome, etc. Existing machine and deep learning-based methods were introduced to forecast genome disorders. However, the genome prediction outcomes were not sufficient. To address this issue, propose a new method called Quadratic Feynman Polynomial Interpolated and Vector Nearest Centroid-based (QFPI-VNC) for acutely predicting the genome disorder with improved sensitivity and specificity. First, we utilized medical data about children from a public genomes dataset and applied it to Linear Quadratic and Feynman Kac Genome filtering to obtain computationally efficient filtered results. Next, the results are fed to the Concordance Correlated Polynomial Interpolation with the purpose of extracting genome wide data in an accurate manner. Finally, the features extracted are fused and fed to the Support Vector and Nearest Centroid model for genome disorder prediction. Experimental investigations of the proposed method employing the genome dataset confirm that the performance of the proposed method is prospective and in the scope of acceptance with relative to state-of-the-art methods in terms of convergence speed, recognition rate, sensitivity, and specificity. Results suggest that the QFPI-VNC method produces the best performance with a higher genome disease detection rate by 14%, accuracy by 11%, sensitivity by 14% specificity by 12%, and lesser convergence speed by 29% than compared to state-of-the-art methods.