INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Breast cancer is a significant public health concern, necessitating advanced techniques for early detection and prediction. This research paper investigates the application of machine learning algorithms, specifically Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Residual Networks (ResNet), for breast cancer prediction using a breast cancer dataset in CSV format. The dataset comprises clinical and diagnostic features, making it suitable for both traditional and deep learning models. SVM, a powerful classification algorithm, is employed to analyze the tabular data. The study assesses the algorithms' performance based on
accuracy, sensitivity, specificity, and confusion matrix to determine their predictive capabilities. The findings reveal the comparative strengths and weaknesses of SVM, CNN, and ResNet in breast cancer prediction using this CSV dataset. This research contributes to enhancing the accuracy of breast cancer prediction models.
Keywords:
Breast cancer prediction, Machine learning, Support Vector Machines, Convolutional Neural Networks, Residual Networks, Comparative analysis, CSV dataset, Early detection, Deep learning.
Cite Article:
"Breast Cancer Prediction using Machine Learning and Deep Learning Algorithms", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a576-a582, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401066.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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