Human Gene Characterization and Pedigree Analysis for Genetic Disease Prediction Using Machine Learning

Abstract

A disorder is a disease that disrupts the regular operation of any part of the human body. Some gene mutations lead towards genetic disorders. Autosomal Dominant Disorder and Autosomal Recessive Disorder are two forms of genetic disorders. This study categorized genetic disorders into Single Gene Inheritance Disorder, Multifactor Genetic Disorder, and Mitochondrial Genetic Disorder. In single-gene inheritance, the mother or the father is affected, and their genome has a genetic mutation that causes a specific genetic condition. The interaction of different environmental factors, such as radiation, pollution, medicines, and smoke exposure, among others, with muted genes resulted in a mutation that could lead to a multifactor genetic disorder. Almost 1 in 213 children are affected by any gene mutation. The increasing prevalence of genetic diseases demands proper measures for early identification, which could help to tackle and lessen the damage. Traditional identification is time-consuming and expensive, so it's elementary to miss the signs of early disease. The subjectivity of geneticists for the interpretation of the same disease can be different. There is a critical need for early-stage detection of genetic diseases. Different researchers have deployed many AI, ML, and DL methods for early Classification and identification of genetic diseases, which have proved cost-effective and time-saving. These algorithms are intended to reveal related information from data that could assist in clinical decision-making. Keeping in view the problems mentioned above, this study automated the process of Classification and detection of major multi-genetic diseases such as Leigh Syndrome, Tay-Sachs, CS, Diabetes, LHON, Hemochromatosis and Mitochondrial Myopathy using KNN, LR, DT, RFC, Multinomial Naïve Bayes, and Gaussian Naïve Bayes using six different Machine learning algorithm with the aim of accuracy improvement. The proposed models achieve accuracy of 98%, 26%, 70%, 97%, 27% and 25 %, respectively. The Proposed system will further help geneticists make diagnostic decisions.