Feasible analysis of gene expression –a computational based classification for breast cancer

•To diagnose the breast cancer at the earliest to reduce the mortality rate.•Computational method on fuzzy based logistic regression to predict the breast cancer.•Inefficiency of feature selection of gene data is solved by LASSO Logistic Regression (LLR).•Computational Tractable is performed by Maximum Likelihood Estimation (MLE).•Missing Parameters issues are solved by Expectation Maximization (EM) algorithm. Computational based classification of gene expression data for analyzing the genetic pattern provides better clinical prediction for breast cancer. Breast cancer is one of the leading cancers among women all over India. This type of cancer leads to malignant tumor developed in the breast. Recent methodologies have undergone many classification methods to analyze the characteristics of gene expression. This paper focuses on computational method such as fuzzy based logistic regression to predict the expression of the gene data. In order to bring accuracy and to solve the inefficiency in feature selection of gene expression data, LASSO Logistic Regression (LLR), a novel methodology is implemented. For computational tractable, Maximum Likelihood Estimation (MLE) is implied with regression model. Diagnosis and prognosis of breast cancer becomes a great challenge in the medical era. This research work explores the mining technology based algorithm to classify the cancer data using fuzzy methodology by evaluating few samples of gene expression data of breast cancer as a training set and the resultant test data are validated to predict the cancer at the earlier stage. For feasible analysis, Expectation Maximization (EM) algorithm is deployed for unknown or missing parameters of gene data expression.