An adaptive framework for predicting breast cancer at an early stage

Breast cancer is one of the leading medical problems in the healthcare field among women. The cancer-related death rate is a major global health problem, particularly in developing countries. Early diagnosis of breast cancer is the only effective way to deal with this mortality factor. Although there are many methods for preventing cancer, there are still some types that have unknown cures. Breast cancer is extremely common and can be effectively treated if caught in its early stages. A correct diagnosis of breast cancer is a vital first step in treatment. Predicting the subtype of breast cancer is an active area of study. In this article, an attempt has been made to present a framework to predict breast cancer using machine learning techniques at an early stage to get the best treatment for the patient. The proposed method uses the machine learning classifiers Logistic Regression and Support Vector Machine to classify breast cancer patients into benign (Non Cancerous Tumor) and salignant (Cancerous Tumor) categories. The proposed mechanism is implemented using Python and Jupyter Notebook on the real dataset, which is generated through a sensing device and collected from the UCI repository. The performance is analyzed using performance metrics such as accuracy, precision, F-measure, etc. The Logistic Regression (LR) model achieves an accuracy of 97.14%, whereas, in the SVM model, the obtained accuracy is 96%. The performance of the proposed framework was evaluated and compared with the other algorithms, and the results indicated that the proposed framework achieved better performance than other models