Integration of ML Techniques for Early Detection of Breast Cancer: Dimensionality Reduction Approach

Abstract

Nowadays, the diagnosis of breast cancer (DBC) helps doctors make early detection of breast cancer into non-cancerous (benign B) and cancerous (malignant M). Therefore, using machine learning (ML) algorithms is a solution to diagnosing and predicting symptoms related to DBC. The increased computational complexity, data size, overfitting, and longer training times harm early diagnosis accuracy. In this paper, propose a dimensionality reduction model integrating PCA and KNN for early breast cancer detection. which is used to diagnose and predict breast cancer (DPBC) based on reduced data size by selecting the best features that capture most of the variance in the data. The performance of the proposed model is evaluated with indices such as accuracy, precision, and f-score. Results for the DPBC model were obtained by using the Breast Cancer Wisconsin medical datasets (BCW).