Machine learning-based classification of breast cancer and its detection is
possible without toxic therapy by a well-trained model. The machine learning model
detects features and patterns form the data sets that used when training model which is
useful for detecting tumor and classify whether it is a benign or malignant and this
process simplifies the cancer detection and gives results accurately at a faster rate when
compared to the other traditional methods like Magnetic resonance imaging (MRI),
Coronary artery disease (CAD), Modalities using ultrasound, etc. Here I am proposing
a new technique through which breast cancer can be easily detected by a proper
training model with the help of few classifying algorithms in this research a good set of
data is used for training classifier machine algorithms in Microsoft azure by comparing
all those five algorithms accuracy and working these are the five algorithm models are
2-class Support vector machine, 2-class Neural Networks, 2-class Boosting Tree, 2-
Class Logistic Regression, 2-Class Bayes Point and acquired better results which can
lead and helpful for detecting cancer in future by using machine learning and deep
learning techniques.
Keywords: Benign, Deep Neural Networks, Malignant, Modality, Simple Neural
Networks, Tumor.
