Performing Uni-variate Analysis on Cancer Gene Mutation Data Using SGD Optimized Logistic Regression
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Ashok Reddy Kandula, Dr. R. Sathya, Dr. S. Narayana
|DOI : 10.14445/22315381/IJETT-V69I2P209|
MLA Style: Ashok Reddy Kandula, Dr. R. Sathya, Dr. S. Narayana "Performing Uni-variate Analysis on Cancer Gene Mutation Data Using SGD Optimized Logistic Regression" International Journal of Engineering Trends and Technology 69.2(2021):59-67.
APA Style:Ashok Reddy Kandula, Dr. R. Sathya, Dr. S. Narayana. Performing Uni-variate Analysis on Cancer Gene Mutation Data Using SGD Optimized Logistic Regression. International Journal of Engineering Trends and Technology, 69(2), 59-67.
There exists a problem in selecting the appropriate machine learning model for any given domain-specific data. Still, researchers are having issues over the model selection in solving the business problem. Along with model selection issues, researchers also face problems in the dataset. Provided all features separating important features and unimportant features in predicting the target class is a challenging task. This paper resolves these issues by using univariate data analysis through machine learning classification techniques as a basic analysis in the process of learning about the data. The objective of the paper is to perform a multi-class classification technique on different classes of mutation effects for the discussed genes. An advanced machine learning-based univariate analysis is performed on each dependent feature to get information about the data. In this paper, we proposed an optimized logistic regression technique using a stochastic gradient optimizer to perform the prediction of target classes. The model prediction is evaluated with a multiclass log loss metric.
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Univariate analysis, Prediction, Mutation changes, Logistic regression, Stochastic Gradient Descent.