THE EFFECT OF FEATURE SELECTION METHODS ON MACHINE LEARNING MODEL PERFORMANCE: A COMPARATIVE STUDY FOR BREAST CANCER PREDICTION

Developing countries often face a high incidence of breast cancer, making early detection vital for effective treatment. The risk of developing breast cancer can be evaluated using machine learning methods and regular diagnostic data. In cancer datasets, there is a wealth of patient information, but not all of it is valuable for predicting cancer. This highlights the significance of feature selection methods in uncovering the relevant data. In this field, many studies have attempted to predict the different types of breast tumours, since it is important to diagnose breast cancer medication accurately. This paper aims to perform a comparison such that to show the effect of different feature selection methods on the accuracy of various existing machine learning algorithms. The study focuses on seven machine learning algorithms: K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Trees (DT), Support Vector Machines (SVM), Logistic Regression (LR), Neural Network (NN), and Random Forest (RF). The feature selection techniques examined include F-test Feature Selection, Mutual Information (MI), and Spearman Correlation Coefficient. The dataset used for the experiments is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, which is publicly available from the UCI Repository. The findings reveal that when feature selection is implemented, the LR and NN algorithms demonstrate superior accuracy and perform exceptionally well across other metrics compared to the other models