Published Paper Details:

Breast cancer imaging characteristics and patient-related data make up the study's extensive dataset. Tumor traits, patient demographics, and medical history are all part of these features, which comprise a variety of therapeutically significant aspects. Next, we use this dataset to train and test SVM and KNN models. We want to see how well they can distinguish between benign and malignant tumors. Classification accuracy, sensitivity, and specificity are just a few of the performance indicators used to assess each model's efficacy. You can learn a lot about the models' accuracy in breast cancer case classification and tumor type discrimination from these measures. This research highlights the strong performance of SVM and KNN algorithms in detecting breast cancer, which is important. This exemplifies the promise of machine learning methods to greatly improve medical diagnostics. More accurate and advanced breast cancer detection techniques are in the works, and this study hopes to add to the existing body of knowledge in the area. These techniques have the potential to enhance patient care by allowing for earlier identification, which in turn improves treatment results. In the end, the study aims to connect machine learning with clinical practice, providing useful insights that may guide the creation of personalized breast cancer diagnostic plans for patients. Researchers, physicians, and healthcare providers are working together to make these breakthroughs so that healthcare professionals can fight breast cancer better. Patients all across the globe will benefit from this.