Machine learning techniques like Regression are used to study the relationship between risk factors and diseases. This study compared Bridge and Elastic Net regressions to see which one handles multicollinearity better in analyzing multiple variables. The researchers used breast cancer data for comparison and found that Bridge regression is more effective at dealing with multicollinearity, with a VIF value of 1.182296 compared to Elastic Net's 1.204298. For the best model fit, Bridge regression with a certain parameter performed better with lower MSE, AIC, and BIC values. Both Bridge and Elastic Net regressions can help in medical fields by improving diagnosis and treatment.