Using Fine Needle Aspiration Data to Classify Breast Cancer Types by Machine Learning
DOI:
https://doi.org/10.33977/2106-000-008-003Keywords:
Machine Learning (ML), , Breast Cancer Classifications, Decision Tree Classifier (DTC), , Support Vector Machine (SVM), , Random Forest Classifier (RFC), , Fine Needle AspirationAbstract
Objectives: Breast cancer, a leading cause of death worldwide and the foremost in Palestine, often benefits from early diagnosis to improve patient outcomes. However, diagnosing small tumors accurately can be challenging, with a high risk of human error. This study seeks to enhance breast cancer classification by utilizing machine learning (ML) algorithms.
Methods: The research analyzed and utilized three machine learning techniques - Decision Tree Classifier (DTC), Support Vector Machine (SVM), and Random Forest Classifier (RFC) - to predict breast cancer tumors. The accuracy of the three algorithms was analyzed and evaluated using a confusion matrix as well as different metrics on a dataset containing 569 samples and 29 features.
Results: The result showed that the Decision Tree Classifier (DTC) has the high scores of 100% in accuracy, precision, sensitivity, and specificity.
Conclusions: In the conclusion, the research emphasizes the excellent performance of the Decision Tree Classifier in classifying breast cancer, which could significantly improve diagnostic accuracy and patient outcomes. The results indicate that DTC has the potential to be a useful ML model in decreasing human diagnostic mistakes and enhancing the early detection and care in medical environments, prompting additional studies to enhance and confirm its effectiveness.
Methods: The research analyzed and utilized three machine learning techniques - Decision Tree Classifier (DTC), Support Vector Machine (SVM), and Random Forest Classifier (RFC) - to predict breast cancer tumors. The accuracy of the three algorithms was analyzed and evaluated using a confusion matrix as well as different metrics on a dataset containing 569 samples and 29 features.
Results: The result showed that the Decision Tree Classifier (DTC) has the high scores of 100% in accuracy, precision, sensitivity, and specificity.
Conclusions: In the conclusion, the research emphasizes the excellent performance of the Decision Tree Classifier in classifying breast cancer, which could significantly improve diagnostic accuracy and patient outcomes. The results indicate that DTC has the potential to be a useful ML model in decreasing human diagnostic mistakes and enhancing the early detection and care in medical environments, prompting additional studies to enhance and confirm its effectiveness.
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