A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO

Abstract

Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in developing countries like India. Genetic test takes between 2 and 4 weeks to decide the cancer. The time duration suffers the prognosis of genes because some patients have high rate of cancerous cell growth. In the research work, a cost and time efficient method is proposed to predict the gene expression level on the basis of clinical outcomes of the patient by using machine learning techniques. An improved SVM-RFE_MI gene selection technique is proposed to find the most significant genes related to breast cancer afterward explained variance statistical analysis is applied to extract the genes contain high variance. Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression techniques are used to predict the gene expression level. The proposed method predicts the expression of significant genes with reduced Root Mean Square Error and acceptable adjusted R-square value. As per the study, analysis of these selected genes is beneficial to diagnose the breast cancer at prior stage in reduced cost and time.

Bioinformatics‐based analysis of the relationship between plasminogen regulatory genes and photoaging

“…The LASSO regression algorithm is accomplished using the R package “glmnet” to identify the genes connected with the diagnostic ability of photoaging and control samples 11 . Support Vector Machine‐Recursive Feature Elimination (SVM‐RFE), a supervised machine learning algorithm widely used in classification and regression analysis, was used to filter the best genes from the data cohort in order to avoid overfitting 12 . The genes acquired from both algorithms were intersected to identify the key genes for diagnosing photoaging.…”

“…11 Support Vector Machine-Recursive Feature Elimination (SVM-RFE), a supervised machine learning algorithm widely used in classification and regression analysis, was used to filter the best genes from the data cohort in order to avoid overfitting. 12 The genes acquired from both algorithms were intersected to identify the key genes for diagnosing photoaging. The GeneMANIA database (https: //genem ania.…”

Breast Cancer Classification by Gene Expression Analysis using Hybrid Feature Selection and Hyper-heuristic Adaptive Universum Support Vector Machine

“…This model achieved an accuracy of 89.40% with an improvement of 5.44% on TCGA datasets but did not consider the class imbalance problem. Gupta and Gupta [25] presented an improved SVM-RFE gene selection scheme with the Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression for classifying breast cancer genes. This method reduced the RMSE values from 0.15 to 0.24.…”

An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

“…The proposed integrated ML scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to generate complete and adequate model prediction and important risk factor identification results. XGB and Lasso methods are both widely and successfully used techniques in breast cancer/mammography researches [ 17 , 18 , 19 , 20 ]. They are also commonly used approaches to selecting the critical predictor variables in healthcare and medical informatics applications [ 21 , 22 ].…”