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@article{203497,
        author = {HASNA M},
        title = {ADVANCEMENTS IN BREAST CANCER DETECTION: HARNESSING THE POWER OF MACHINE LEARNING ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11371-11376},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203497},
        abstract = {Breast Cancer (BC) stands as a pervasive global health concern and remains the foremost cause of mortality among women worldwide. Notably, a significant proportion of individuals afflicted by breast cancer lack any familial predisposition. The escalating incidence of breast cancer underscores the urgency of addressing it as a prevalent public health issue. With women facing approximately a 1/8 probability of a breast cancer diagnosis, factors such as aging, genetic predisposition, dense breast tissues, obesity, and exposure to radiation contribute to elevated risk. Distinguishing between malignant and benign tumours is paramount for accurate diagnosis, necessitating a reliable diagnostic procedure. Mammography currently serves as the primary method for breast cancer detection, supplemented by Fine Needle Aspiration Cytology (FNAC) in the diagnostic process. This paper explores the integration of machine learning models for early prediction of breast cancer, recognizing the pivotal role of early diagnosis in enhancing treatment success rates. The study compares the prediction accuracy and performance parameters of various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Decision Tree, and KNearest Neighbours (KNN).). Through a comprehensive comparative analysis, the research aims to identify the most effective algorithm based on classifier performance, contributing valuable insights to the optimization of breast cancer detection methodologies.},
        keywords = {Fine Needle Aspiration Cytology, Convolutional Neural Network, Support Vector Machine, Mammography},
        month = {May},
        }