An Applied Artificial Intelligence Aided Technique for Effective Classification of Breast Cancer

Among Women, Breast cancer is one of the maximum occurring diseases. Many women die every year because of breast cancer globally. Early prediction and diagnosis of this disease can prevent death in the end. The survival rate increases on detecting breast cancer early as better treatment can be provided. Development in prediction and diagnosis is necessary for the life of people. A higher amount of accuracy in the prediction of breast cancer is necessary for the treatment aspects and also for the survivability of patients. It is apparent that there are different techniques available in breast cancer detection but machine learning algorithms can bring a large contribution to the process of prediction and early diagnosis of breast cancer. In this study, we use a Wisconsin dataset which was collected from a scientific dataset of 569 breast cancer. Out of 569 patients, 63% were diagnosed with benign and 37% were diagnosed with malignant cancer. The benign tumor grows slowly and does not spread. We apply five machine learning algorithms to this dataset and train a model for predicting malignant and benign tissues (BCs). Algorithms are K-Nearest neighbor, Support vector machine, Decision tree, Deep learning, and Random-forest respectively. The effectiveness of these algorithms is evaluated in terms of accuracy, F measure, confusion matrix, and specificity. By comparing the results deep learning classifier gives the highest accuracy and outclass all the other classifiers by attaining an accuracy of 9S.l3%. SVM gives an accuracy of 97.66% whereas KNN gives an accuracy of 95.79% etc.