Intelligent Model for the Early Detection of Breast Cancer Using Fine Needle Aspiration of Breast Mass

Breast cancer is a type of cancer that forms in the cells of the breasts. It can occur in both men and women, but it is far more common in women with an increase likelihood of development caused by some risk factors like age, family history, gene mutations, hormonal factors, and exposure to estrogen. There are several essential methods for early diagnostic and detection, like regular breast self-examinations, clinical breast examination mammograms, etc. Breast cancer detection faces several challenges that span various stages from screening to diagnosis. It is in the light of these, that the drive to overcome these challenges faced by patients and medical experts to get timely diagnosis or detection of breast cancer that motivated this research. The work aims at developing an intelligent-based system for the early detection of breast cancer driven by machine learning algorithm using features obtained from a digitized image of the Fine Needle Aspiration (FNA) of breast mass. The Machine learning algorithm used was the Support Vector Machine (SVM) and was apply to the Breast Cancer dataset. Additionally, feature selection and a neural network model was applied to improve the performance of the system. This work demonstrates that SVM algorithm can be used to enhance the early detection of breast cancer. Compared with the 92% accuracy of mammography, this machine learning model achieved a higher accuracy of around 98.18% for breast cancer detection and diagnosis based on the dataset that was used.