The Analysis of Breast Cancer Classification Involves Utilizing Machine Learning (Ml) Techniques and Hyperparameter Adjustment - A Comparative Study
DOI:
https://doi.org/10.59994/ajamts.2024.2.10Abstract
This study aims to analyze and classify breast cancer (BC) cases using machine learning (ML) techniques and hyperparameter tuning. The BC dataset from the University of California (UCI) was utilized, which comprises 569 cases classified as malignant (M) and benign (B), with 32 features. The algorithms employed in the study included Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Decision Tree (DT), and Gaussian Naive Bayes (NB). The results indicated that the SVC algorithm performed the best, achieving an accuracy of 98% on the test set, along with a precision of 100%. Furthermore, all algorithms demonstrated high performance, reflecting the effectiveness of machine learning techniques in classifying breast cancer cases.
Keywords:
Breast Cancer (BC), Breast Cancer Diagnosis, Cancer Dataset, Machine Learning, ; Support Vector Classification (SVC), K-Nearest Neighbor Regression (KNN), Logistic Regression (LR)References
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