ANALYSIS OF DIFFERENT MACHINE LEARNING TECHNIQUES WITH PCA IN THE DIAGNOSIS OF BREAST CANCER

Abstract

In recent years, different types of cancer cases are common. In addition to being the most common cancer among women today, breast cancer has surpassed lung cancer as the most common cancer type in the world since 2021. The fact that early diagnosis greatly reduces the risk of death in breast cancer necessitated the use of computer-aided systems in these processes. These systems are extremely important in terms of being an assistant to the expert opinion. In this study, we reduced our dataset to 171 data using Principal Component Analysis (PCA) to accelerate disease diagnosis on the Wisconsin Breast Cancer dataset and 2 different classification processes were performed using 5 different machine learning. The success rate of each algorithm was compared and it was revealed that Logistic Regression was the most successful method with an accuracy rate of 98.8% after PCA