Authors :
Abisola Mercy Olowofeso; Stanley T Akpunonu; Olamide Shakirat Oni; Caleb Ayooluwa Sawe
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/yjd272n6
Scribd :
https://tinyurl.com/2axad7mv
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY2174
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Breast cancer remains a significant health
concern globally, with early detection being crucial for
effective treatment. In this study, we explore the
predictive power of various diagnostic features in breast
cancer using machine learning techniques. We analyzed
a dataset comprising clinical measurements of
mammograms from 569 patients, including mean radius,
texture, perimeter, area, and smoothness, alongside the
diagnosis outcome. Our methodology involves
preprocessing steps such as handling missing values and
removing duplicates, followed by a correlation analysis
to identify and eliminate highly correlated features.
Subsequently, we train eight machine learning models,
including Logistic Regression (LR), K-Nearest
Neighbors (K-NN), Linear Support Vector Machine
(SVM), Kernel SVM, Naïve Bayes, Decision Trees
Classifier (DTC), Random Forest Classifier (RFC), and
Artificial Neural Networks (ANN), to predict the
diagnosis based on the selected features. Through
comprehensive evaluation metrics such as accuracy and
confusion matrices, we assess the performance of each
model. Our findings reveal promising results, with 6 out
of 8 models achieving high accuracy (>90%), with ANN
having the highest accuracy in diagnosing breast cancer
based on the selected features. These results underscore
the potential of machine learning algorithms in aiding
early breast cancer diagnosis and highlight the
importance of feature selection in improving predictive
performance.
Keywords :
Cancer, Breast Cancer, Machine Learning, Artificial Intelligence.
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Breast cancer remains a significant health
concern globally, with early detection being crucial for
effective treatment. In this study, we explore the
predictive power of various diagnostic features in breast
cancer using machine learning techniques. We analyzed
a dataset comprising clinical measurements of
mammograms from 569 patients, including mean radius,
texture, perimeter, area, and smoothness, alongside the
diagnosis outcome. Our methodology involves
preprocessing steps such as handling missing values and
removing duplicates, followed by a correlation analysis
to identify and eliminate highly correlated features.
Subsequently, we train eight machine learning models,
including Logistic Regression (LR), K-Nearest
Neighbors (K-NN), Linear Support Vector Machine
(SVM), Kernel SVM, Naïve Bayes, Decision Trees
Classifier (DTC), Random Forest Classifier (RFC), and
Artificial Neural Networks (ANN), to predict the
diagnosis based on the selected features. Through
comprehensive evaluation metrics such as accuracy and
confusion matrices, we assess the performance of each
model. Our findings reveal promising results, with 6 out
of 8 models achieving high accuracy (>90%), with ANN
having the highest accuracy in diagnosing breast cancer
based on the selected features. These results underscore
the potential of machine learning algorithms in aiding
early breast cancer diagnosis and highlight the
importance of feature selection in improving predictive
performance.
Keywords :
Cancer, Breast Cancer, Machine Learning, Artificial Intelligence.