Research Based Exploration of Breast Cancer data ease on Machine Learning Outlook
DOI:
https://doi.org/10.52783/jns.v14.2422Keywords:
Decision Tree, Random Forest, Prediction, Logistic Regression, MLAbstract
Paste your textual content right here and click on "Next" to watch this article rewriter do it is thing. Breast most cancers is a full-size fitness concern, necessitating correct prediction fashions for early detection and accelerated affected person outcomes. This learn about gives a comparative evaluation of three computer mastering models, namely, Logistic Regression, Decision Tree, and Random Forest, for breast most cancers prediction the usage of the Wisconsin breast most cancers diagnostic dataset. The dataset includes elements computed from first-class needle aspirate pix of breast masses, with 357 benign and 212 malignant cases. The research findings spotlight that the Random Forest model, leveraging the pinnacle five predictors—“concave points_mean”, “area_mean”, “radius_mean”, “perimeter_mean”, and “concavity_mean”, achieves the best possible predictive accuracy of about 95% and a cross-validation rating of about 93% for the take a look at dataset. These effects display the plausible of laptop mastering strategies in breast most cancers prediction, underscoring their significance in assisting early detection and diagnosis.
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