Breast cancer is a serious disease that affects both men and women, although women are more susceptible. It is one of the leading causes of cancer-related deaths, particularly among women. In its early stages, breast cancer often presents few observable signs, making diagnosis challenging and delaying effective treatment. However, it is important to note that with early diagnostics and appropriate intervention, the mortality rate from this disease can be significantly reduced. In addition to standard diagnostic methods, artificial intelligence has shown great potential in assessing the early risk of breast cancer by analyzing enriched health data. In this study we attempted to see accuracy with and without PCA, apply Logistic Regression to classify the cancer. The results shows an in-sight that with PCA the best results are obtained as the most useful information is restrained in the data. Subsequent research will aim to combine fuzzy logic controllers with rule-based controllers to improve the detection rate of breast cancers. Additionally, expert rules will be utilized to evaluate the validity of the proposed model.