Authors :
Krish Kapoor
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/k9z2pp6v
DOI :
https://doi.org/10.5281/zenodo.8297612
Abstract :
Breast cancer is a prevalent form of cancer
that affects a significant number of individuals
worldwide and can have severe consequences if not
detected and treated early. The World Health
Organization (WHO) estimates that breast cancer is the
most common cancer among women globally, with an
estimated 2.3 million new cases in 2020 alone. Early
detection is crucial in improving survival rates and
treatment outcomes. This paper explores the application
of Machine Learning (ML) techniques for predicting
breast cancer diagnosis in individuals. We utilize a
publicly available dataset from the Kaggle machine
learning repository, which contains data from breast
cancer patients collected from various medical
institutions. Several machine learning models, including
Naive Bayes Algorithm, Decision Trees, Logistic
Regression, Neural Networks, Random Forest, Stochastic
Gradient, and Support Vector Machines, are employed
to analyze the dataset. The performance of these models
is assessed using 10-fold cross-validation. Furthermore,
we propose the most suitable machine learning algorithm
for breast cancer diagnosis based on specified input
parameters and discuss the potential deployment of a
breast cancer diagnostic tool.
Keywords :
Breast Cancer Detection, Supervised and Unsupervised Machine Learning, Artificial Intelligence.
Breast cancer is a prevalent form of cancer
that affects a significant number of individuals
worldwide and can have severe consequences if not
detected and treated early. The World Health
Organization (WHO) estimates that breast cancer is the
most common cancer among women globally, with an
estimated 2.3 million new cases in 2020 alone. Early
detection is crucial in improving survival rates and
treatment outcomes. This paper explores the application
of Machine Learning (ML) techniques for predicting
breast cancer diagnosis in individuals. We utilize a
publicly available dataset from the Kaggle machine
learning repository, which contains data from breast
cancer patients collected from various medical
institutions. Several machine learning models, including
Naive Bayes Algorithm, Decision Trees, Logistic
Regression, Neural Networks, Random Forest, Stochastic
Gradient, and Support Vector Machines, are employed
to analyze the dataset. The performance of these models
is assessed using 10-fold cross-validation. Furthermore,
we propose the most suitable machine learning algorithm
for breast cancer diagnosis based on specified input
parameters and discuss the potential deployment of a
breast cancer diagnostic tool.
Keywords :
Breast Cancer Detection, Supervised and Unsupervised Machine Learning, Artificial Intelligence.
