Evaluation of Missing Data Imputation Methods and PCA Techniques for Machine Learning Models in Breast Cancer Diagnosis Using WBCD

Cancer is one of the leading causes of human mortality and breast cancer deaths are particularly common among women. Early diagnosis of breast cancer is considered a key way to reduce these deaths. The use of expert systems, artificial intelligence and machine learning techniques in the medical field aims to assist doctors in early disease detection. One of the main objectives of these technologies is to diagnose life-threatening diseases such as breast cancer earlier and more accurately. In this study, analyses conducted on the Wisconsin Breast Cancer Dataset (WBCD) evaluated the effects of different missing data imputation methods and PCA-based data reduction technique on model performance using supervised machine learning methods. In the first stage of the study, the detection and management of missing values in the dataset were addressed. It was found that imputing missing values with median performed better compared to other methods. Subsequently, the dataset was reduced in size using the PCA method and the performance of algorithms was measured by experimenting with different numbers of components. The results indicate that effectively addressing the missing data problem and using PCA-based data reduction techniques significantly improve model performance. The best performance was achieved by imputing missing data with median values and reducing data dimensionality with PCA. This study emphasizes the importance of combining machine learning approaches for breast cancer diagnosis with missing data management strategies. Additionally, the effects of different missing data imputation methods and PCA on model performance have been thoroughly examined.