You have no items in your shopping cart.
ABSTRACT
Breast cancer is the most common type of cancer among women and the major cause of cancer-related deaths in this population. Generally speaking, women over 50 are found to have breast cancer between 50% and 80% of the time. However, due to inadequate nutrition, it now appears earlier in life. For health care providers, making a diagnosis can be extremely difficult. Early detection will give us more time to develop more effective treatments and expedite the search for a cure. Cancer is the second most common cause of mortality worldwide. For women, breast cancer is the leading cause of premature death. Many different kinds of studies on early breast cancer diagnosis have been carried out recently in an effort to increase the likelihood of successful therapy and treatment-free survival. The vast majority of the studies concentrated mainly on mammography images. Mammograms can produce both false positives and false negatives, both of which can be detrimental to the patient's health. Computer-assisted diagnosis was employed to fix this issue. In this case, mammography was the method of choice for detecting breast cancer. It's critical to search for other methods that can produce a more accurate forecast. The way that breast cancer is classified has been significantly impacted by machine learning. One of the constraints of current models is that they require a lot of data in order to make reliable predictions. More complex algorithms, such Support Vector Machines (SVM), have been used as solutions to these outdated models in order to improve classification accuracy. These methods need to be less expensive, less risky, and simpler to use with different kinds of data. This study employed the Support Vector Machine (SVM) to identify breast cancer. Findings showed that, when compared to related research, the StandardScalar+SVM combo performed encouragingly, obtaining an accuracy of 97.66%. When the SVM classifier's performance was compared to that of other relevant studies, it demonstrated better accuracy and dependability. This study shows how well SVM can detect breast cancer and implies that these techniques can be applied for accurate and dependable breast cancer detection.
