Designed Early Breast Cancer Detection and Classification Model using Machine Learning Approaches: Case of St. Paul’s Specialized Hospital
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Abstract
Breast cancer is the second leading cause of death for women worldwide, and one of the most significant causes of death for women. Accordingly, radiologists spend a lot of time and energy manually detecting breast cancer from mammography images. In the case of breast cancer diagnoses at the hospital, the amount of data that could be helpful increases, but remains unused. These mammogram images are underutilized due to technological advances such as image processing and artificial intelligence, despite their usefulness if properly organized, analyzed, and presented to healthcare experts. This study aims to develop an early breast cancer detection and classification model based on unweighted image data collected during the 2017-2020 period at St. Paul's Hospital. In the meantime, we employed logistic regression, support vector machines, decision trees, and random forest algorithms. An evaluation of the proposed model was conducted using confusion matrices. After the training process had been completed, the model was evaluated by running a test dataset on the classifier to compare its output to the observed data based on micro averages, precision, recall, and f1-score values. As a result, the accuracy of the Logistic Regression (LR) model was 94.93%, the Support Vector Machine model (SVM) was 95.91%, the Decision Tree (DT) model was 92.59%, and the Random Forest (RF) model was 95.52%. Therefore, developing the model to detect and classify breast cancer diseases, we selected a SVM classifier, working on its efficiency. Thus, the implications of this study may have allowed the radiologist to identify and make the right decision based on the outcome results in the future.
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