Authors :
A. Anu Priya; T. Pramoth Krishnan; Dr. C. Suresh
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/ps8ktpxn
Scribd :
https://tinyurl.com/53apdh7m
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR845
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Detecting breast cancer early is crucial for
improving patient survival rates. Using machine learning
models to predict breast cancer holds promise for
enhancing early detection methods. However, evaluating
the effectiveness of these models remains challenging.
Therefore, achieving high accuracy in cancer prediction
is essential for improving treatment strategies and patient
outcomes. By applying various machine learning
algorithms to the Breast Cancer Wisconsin Diagnostic
dataset, researchers aim to identify the most efficient
approach for breast cancer diagnosis. They evaluate the
performance of classifiers such as Random Forest, Naïve
Bayes, Decision Tree (C4.5), KNN, SVM, and Logistic
Regression, considering metrics like confusion matrix,
accuracy, and precision.
The assessment reveals that Random Forest
outperforms other classifiers, achieving the highest
accuracy rate of 97%. This study is conducted using the
Anaconda environment, Python programming language,
and Sci-Kit Learn library, ensuring replicability and
accessibility of the findings. In summary, this study
demonstrates the potential of machine learning
algorithms for breast cancer prediction and highlights
Random Forest as the most effective approach. Its
findings contribute valuable insights to the field of breast
cancer diagnosis and treatment.
Keywords :
Machine Learning Models, Data Exploratory Techniques, Breast Cancer Diagnosis, Tumors Classification.
Detecting breast cancer early is crucial for
improving patient survival rates. Using machine learning
models to predict breast cancer holds promise for
enhancing early detection methods. However, evaluating
the effectiveness of these models remains challenging.
Therefore, achieving high accuracy in cancer prediction
is essential for improving treatment strategies and patient
outcomes. By applying various machine learning
algorithms to the Breast Cancer Wisconsin Diagnostic
dataset, researchers aim to identify the most efficient
approach for breast cancer diagnosis. They evaluate the
performance of classifiers such as Random Forest, Naïve
Bayes, Decision Tree (C4.5), KNN, SVM, and Logistic
Regression, considering metrics like confusion matrix,
accuracy, and precision.
The assessment reveals that Random Forest
outperforms other classifiers, achieving the highest
accuracy rate of 97%. This study is conducted using the
Anaconda environment, Python programming language,
and Sci-Kit Learn library, ensuring replicability and
accessibility of the findings. In summary, this study
demonstrates the potential of machine learning
algorithms for breast cancer prediction and highlights
Random Forest as the most effective approach. Its
findings contribute valuable insights to the field of breast
cancer diagnosis and treatment.
Keywords :
Machine Learning Models, Data Exploratory Techniques, Breast Cancer Diagnosis, Tumors Classification.