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@article{174206,
        author = {Ms.Dharshana R S and Mrs.Vidhya A and Mr. Dinesh S and Mr. Dinesh R},
        title = {IMPROVING BREAST CANCER DETECTION WITH RANDOM FOREST ALGORITHM AND THE BREAKHIS DATASET},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3145-3150},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174206},
        abstract = {Breast cancer is still one of the health issues globally that requires proper and effective diagnostic techniques. This study compares the efficacy of several machine learning models to classify breast cancer histopathological images. BreakHis dataset was used by applying preprocessing methods in the form of image resizing and standardization. Different models, such as Logistic Regression, Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Decision Tree, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on main performance metrics such as accuracy, precision, recall, and F1-score. Out of these, the highest accuracy (83.38%) was obtained with the Random Forest algorithm, which indicates its potential in enhancing breast cancer diagnosis. The results emphasize the significance of medical imaging using machine learning and how it can strengthen automated diagnostic aid systems.},
        keywords = {Breast Cancer Classification, Machine Learning in Healthcare, Histopathological Image Analysis, BreakHis Dataset},
        month = {March},
        }