Published Paper Details:

Nowadays, Breast Cancer is considered to be a major threat to women which is leading to an increase in death rates of women causing a major concern in the society regarding it. This disease may be a major threat but today medical-science is capable enough to cure such threats without causing any harm to women if detected at early stages. Detecting the cancer and differentiating between the diagnosis that affirms whether the patient has breast cancer or not is considered to be the major challenge. For accurate diagnosis of breast cancer many research paper throws light on Machine Learning Algorithms like Support Vector Machine (SVM) and Artificial Neural Network (ANN). The following paper talks about the diagnosis of breast cancer using Support Vector Machine (SVM) with Principal Component Analysis (PCA) which are supervised machine learning techniques for breast cancer detection. The proposed model is tested on Wisconsin breast cancer diagnosis data set which is selected from UCI machine learning repository. The data was pre processed which was followed by feature extraction of the data set using Principal Component Analysis (PCA), the performance of the model was diagnosed using six different machine learning algorithms namely Naive Bayes, Support Vector Machine, Classification and Regression Trees, Logistic Regression, Linear discriminant Analysis and k-nearest neighbours. The technique proposed in the paper achieved an accuracy of 99.30% by Support Vector Machine with Principal Component Analysis.