Prediction of Breast Cancer Using Machine Learning Techniques for Health Data
Aarti Aarti, Saurabh Karling, Pushpendra Kumar Rajput, SURBHI SURBHI, Praveen Kumar Malik · 2024
Breast cancer is a significant health problem, and early detection is crucial for effective treatment and improved survival rates. Computer-aided detection and diagnosis (CAD) technologies have been developed to aid in early detection. More and more, machine learning models are being utilized to study breast cancer data and predict outcomes A potential area of study is the use of machine learning models in breast cancer research, and more investigation in this area is expected to result in new knowledge and advancements in breast cancer detection and care. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset is a well-known dataset used in breast cancer research. In this study, the authors employed various machine learning models to predict breast cancer using the WBCD dataset. The models used in the study included logistic regression, random forest, and naïve Bayes. The logistic regression model was found to have the highest accuracy of all the models tested, achieving 96.5% precision. This is a significant improvement over previous methods reported. The application of such machine learning models in the research for breast cancer has the potential to improve early detection and treatment, and ultimately save lives. It is worth noting that although the logistic regression model achieved the best results in this study, it is equally important to assess and compare the outcomes and thereby performance of multiple models to ensure the most accurate and reliable results.