Application of Classification Techniques on Breast Cancer Prognosis

Abstract

The most prevalent form of cancer for
females is breast cancer in Americans, and
additionally, it is Asia's and the United States'
second most common cause of death among
females. In the United States in 2009, 40,600
persons died from breast cancer, 400 of whom
were men. Clinical breast exams,
radiographs, and ultrasounds are all excellent
methods for testing for breast cancer today. A
strategy for presenting a set of input-output
sets to a network is referred to as supervised
learning. The subsequent model parameters
are updated iteratively to minimize the
discrepancy around system prediction and
real outcomes for training data. Three
classification methods were tested on a breast
cancer database: Probabilistic Learning,
Logistic Regression, and Neural Net.
Experiments demonstrated that Neuro Net
categorization surpasses Tree Based
categorization and Naïve Bayesian
classification in terms of accuracy and
precision for breast cancer early detection.
Although it is established that the use of Ml
techniques can enhance our knowledge of
cancer progression, these methods must be
confirmed before they're able to be employed
in clinical practice. In this paper, we give a
review of contemporary ML techniques used
in cancer progression modelling. The
prediction models talked about here have
been trained using an assortment of
supervised algorithms for machine learning,
as well as varied input features and data. We
have put together an inventory of the most
recent articles that use such methods for
modelling cancer risk or patient outcomes in
context with the increasing desire to employ
ML methods in cancer studies