Abstract
The genetic information expressed through the development of a sequencing model for DNA/RNA proteins using Machine Learning Algorithms is a big exploration and growing need. Basically, this was intended to identify, predict as well as classify gene families based on the DNA sequence with medical anomalies for early diagnosis of genetic variation. This study assessed gene sequences from three DNA sequence text files, including 4380 human, 1682 chimpanzee, and 820 dog DNA. The genetic disorder dataset includes 35 features that were utilized to predict genetic abnormalities across 22083 patient data. Labelling, correlating, exploratory data analysis, and prediction systems were made for both datasets. Prediction systems were made using Logistic Regression, Gaussian Naive Bayes, K Neighbors, Decision Tree, Random Forest, Gradient Boosting, CatBoost, Multinomial Naive Bayes Classifier, and SVC Classifier algorithms. Multinomial Naive Bayes Classifier achieved the best accuracy rate of 94.42% for DNA sequencing dataset, followed by K Neighbour Classifier, Decision Tree Classifier, Random Forest Classifier, and SVC Classifier contributed 71.98%, 74.85%, 86.82% and 79.6% respectively. For the genetic disorder dataset, the best-performing model was CatBoost with a 54.72% R2CV score. As for the R2CV scores, Logistic Regression, Gaussian Naive Bayes, K Neighbors, Decision Tree, Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine and Gradient Boosting Classifier offered 47.36%, 34.16%, 45.27%, 40.83%, 52.36%, 48.89%, 48.75% and 53.34% respectively. Genetic disorders will be classified in the future based on extensive medical history, sequence data, deep learning models, federated machine learning and transfer learning.
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Upadhyay, V., Harbhajanka, S., Pangaonkar, S., Gunjan, R. (2023). Exploratory Data Analysis and Prediction of Human Genetic Disorder and Species Using DNA Sequencing. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 2. FTC 2023. Lecture Notes in Networks and Systems, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-031-47451-4_14
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