ACCURATE BREAST CANCER CLASSIFICATION BY USING ARTIFICIAL INTELLIGENCE ALGORITHMS
Abstract
Breast cancer is a significant cause of mortality for women worldwide, ranking as the second leading cause of death. In 2018, breast cancer accounted for the highest number of cancer-related deaths among women in 40 European countries. While it ranked as the second leading cause of cancer-related deaths in the EU-28, lung cancer held the top position. Detecting breast cancer at an early stage is vital for improving treatment outcomes and survival rates. Data mining has gained popularity as an effective tool for knowledge discovery in various fields, including medicine. Researchers have applied machine learning techniques, such as multiple classifier algorithms, to predict and analyze patient diagnoses using medical datasets. However, a challenge arises due to imbalanced training data, where the probability of not having the disease is higher than having it. This paper focuses on addressing this issue by comparing two distinct AI models: Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). The objective is to develop a suitable and reliable model capable of handling imbalanced datasets and missing values, thereby enhancing the overall performance of the breast cancer prediction model.