European Journal of Molecular & Clinical Medicine

Cancer is described as a very alarming disease among humankind. The second main
reason for death among modern women is Breast cancer. It affects the physical, mental, social
lifestyles of the people. It is possible to treat cancer in the early stages. The importance of cancer
cells classification into begnin and malignant has led to many research areas in the medical field.
Medical practitioners were adopting machine learning techniques to detect, classify, and predict the
malignant tumour effectively. The machine learning algorithms yield better results in the diagnosis
of malignant tissue. The learning algorithm performs well with optimal features. The objective of
this paper is to identify optimal features in Wisconsin breast cancer Diagnostic data. The techniques
used for feature selection here are Light Gradient Boosting Model (LGBM), Catboost and Extreme
gradient boosting (XGB). The optimized features were given to the Naive Bayes classifier and got an
accuracy of 96.49%.