MACHINE LEARNING MODEL FOR BREAST CANCER DETECTION - Rufai, Malik Adeiza; Muhammad, Ahmad Shehu; Garba, Suleiman; Audu, Lovingkindness (FUDMA JOURNAL OF SCIENCES, 2020)
Cancer is the second most common cause of death in the world, especially in developing countries. Cancer is a serious and dangerous disease that is usually caused by abnormal growth of the cell. This abnormal growth causes lumps (tumors). Breast cancer is ranked the fifth cause of cancer deaths worldwide and the second most common form of cancer amongst females. Machine learning is one of the most trending technologies in computing. It entails the automated conversion of data into useable information that can be related with. This Paper is concerned with the use of a classification algorithm like Support Vector Machine (SVM) to develop a model for the detection of breast cancer. The developed model is to select/extract features from dataset and then classify it as either benign (non-cancerous) or malignant (cancerous). Dataset was collected from the Wisconsin database (in which features are computed from a digitalized image of a Fine Needle Aspirate (FNA) of a breast mass) then trained and tested using the SVM classifier for prediction of breast cancer. In the algorithm, we plot each data item as a point in n-dimensional (here n=9), with the value of each feature being the value of a particular coordinate. Classification is performed by finding the hyper-plane that differentiates the two classes.  The model accepts values of attributes as inputs and predicts whether a breast mass is benign or malignant, with an accuracy level of 94.3%. This model is recommended for use in the health sector and also for personal use.
Keywords: Support Vector Machine (SVM), Benign, Malignant, Fine Needle Aspirate (FNA), Machine Learning, Breast Cancer, Medical Images