Hybrid DNA Differential Methylation Machine Learning Technique for Prediction of Mammary Carcinoma

In many parts of the world, breast cancer is the main reason why women pass away. Until the condition has gotten too bad for appropriate therapy, the disease is frequently misdiagnosed. Consequently, minimizing mortality and other associated hazards would be aided by early disease detection. It is tricky to recognize and understand microarray gene expression data, making it difficult to assess and choose the most pertinent group of genes to use as breast cancer indicators. Here, we use a dataset and a variety of machine learning methods, like Random Forest Classification, Naive Bayes, Support Vector Machine, Logistic Regression, and Logistic Regression, to create predictions about their future growth. The Breast Cancer Wisconsin Evaluation dataset and Matlab 2018 were used in our study to create a hybrid machine-learning model for quick breast cancer prediction. In comparison to other machine-learning techniques, the highly effective Gradient Boosting classifier has a success rate of 99.78%. The diagnosis, analysis, and prediction of breast cancer could all be revolutionized by this novel method of prediction.