Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning
Date
2022
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Publisher
Tech Science Press
Abstract
One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data. Furthermore, the complicated genetic disease has a very diverse genotype, making it challenging to find genetic markers. This is a challenging process since it must be completed effectively and efficiently. This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters. Using the patient’s medical history, we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder. To predict and categorize the patient with a genetic disease, we utilize several deep and machine learning techniques such as Artificial neural network (ANN), K-nearest neighbors (KNN), and Support vector machine (SVM). To enhance the accuracy of predicting the genetic disease in any patient, a highly efficient approach was utilized to control how the model can be used. To predict genetic disease, deep and machine learning approaches are performed. The most productive tool model provides more precise efficiency. The simulation results demonstrate that by using the proposed model with the ANN, we achieve the highest model performance of 85.7%, 84.9%, 84.3% accuracy of training, testing and validation respectively. This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’ lives. © 2022 Tech Science Press. All rights reserved.
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Keywords
deep learning, Genetic disorder, machine learning, mitochondrial gene inheritance disorder, single gene inheritance gene disorder
Citation
Nasir, M. U., Khan, M. A., Zubair, M., Ghazal, T. M., Said, R. A., & Hamadi, H. A. (2022). Single and mitochondrial gene inheritance disorder prediction using machine learning. Computers, Materials and Continua, 73(1), 953-963. doi:10.32604/cmc.2022.028958