Authors :
Annapoorna B. R; Kota V. Vishnu; Reena Jasmine Edwin; S Sai Brinda; Shalini Singh
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3X8Okwe
DOI :
https://doi.org/10.5281/zenodo.7537965
Abstract :
Breast cancer is the most frequent cancer in
women worldwide and is also the most lethal. The
reasons for this illness are many and challenging to
identify. Furthermore, the diagnostic technique, which
determines whether the cancer is benign or malignant,
requires substantial work from doctors and physicians.
There are many diagnostic tests possible which can be
conducted by medical professionals to detect it; however,
it has been increasingly strenuous to precisely spot and
acts on its prognosis As a result, in recent years, there
has been a surge in the use of machine learning and
Artificial Intelligence in general as diagnostic tools. ML
seeks to make computer selflearning easier. in lieu of
contingent on explicit pre-programmed rules and
models, it is based on finding patterns in observed data
and creating models to predict outcomes and evaluate
them on performance measure features like accuracy,
precision, and recall. The primary impetus of this review
is to culminate all the antecedent studies of machine
learning algorithms being utilised for breast cancer
prediction. this survey is going to be useful to the
researchers because of the elaborated probe of various
methodologies for undergoing supplemental inquisitions.
Keywords :
Breast Cancer, Medical Diagnosis, Machine Learning, Logistic Regression, KNN, Decision Tree, SVM, Random Forest, Naive Bayes.
Breast cancer is the most frequent cancer in
women worldwide and is also the most lethal. The
reasons for this illness are many and challenging to
identify. Furthermore, the diagnostic technique, which
determines whether the cancer is benign or malignant,
requires substantial work from doctors and physicians.
There are many diagnostic tests possible which can be
conducted by medical professionals to detect it; however,
it has been increasingly strenuous to precisely spot and
acts on its prognosis As a result, in recent years, there
has been a surge in the use of machine learning and
Artificial Intelligence in general as diagnostic tools. ML
seeks to make computer selflearning easier. in lieu of
contingent on explicit pre-programmed rules and
models, it is based on finding patterns in observed data
and creating models to predict outcomes and evaluate
them on performance measure features like accuracy,
precision, and recall. The primary impetus of this review
is to culminate all the antecedent studies of machine
learning algorithms being utilised for breast cancer
prediction. this survey is going to be useful to the
researchers because of the elaborated probe of various
methodologies for undergoing supplemental inquisitions.
Keywords :
Breast Cancer, Medical Diagnosis, Machine Learning, Logistic Regression, KNN, Decision Tree, SVM, Random Forest, Naive Bayes.
