Classification and Prediction of Breast Cancer Based on Support Vector Classifier on Histopathological Images

With the right amount of human intervention, most diseases can be cured if caught early. Unfortunately, most people don't catch their illness until it's chronic. Hence, it contributes to a global rise in mortality rates. Early detection is key to curing many diseases, including breast cancer. If caught early enough, the disease can be treated successfully before it spreads throughout the body. The leading cause of cancer death among women is breast cancer. Early detection of cancer is critical. Breast cancer data may be diagnosed using a number of different Machine Learning methods. In this study, we introduce a Machine Learning framework for developing fully automated breast cancer diagnostic tools. In this study, we introduce the use of Support Vector classifiers in the diagnosis of breast cancer. We present a cross-validation comparison of the SVC and KNN and assess their efficacy. According to the findings, SVC outperforms the KNN classifier (with an accuracy of 94.12%) while having a lower error rate.