Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Random Forest and Linear Regression Algorithms

Abstract The analysis of different types of diseases is an extremal vital task which would help in producing vaccines for that particular type of disease. However, this is a very costly process as to test every disease it would mean to analyze every gene related to that specific disease. This issue of genic analysis is further elevated when different variations of each disease is considered. As such the use of different computational methods is taken into consideration to tackle the task of genic variation identification. This research makes use of Machine Learning algorithms to help in the identification and prediction of Single Nucleotide Polymorphism or more specifically Single Amino Acid Polymorphism. Taking into consideration ten different types of diseases, this research makes use of Random Forest and Linear Regression algorithms to identify and predict different genic variations of these diseases. From the extensive research, this article concludes that Random Forest algorithm performs better in comparison to Linear regression in genic variation predictions.