Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Analysis of Machine Learning and Deep Learning to Predict Breast Cancer Subject The topic of the resource Accuracy; Breakhis; Breast Cancer; Classifier; Deep Learning; Machine Learning; WBCD; Wisconsin Description An account of the resource According to the report published by American Cancer Society, breast cancer is currently the most prevalent cancer in women. In addition, it is the second leading cause of death. It needs to be taken into serious consideration. Earlier and faster detection can help in the earlier and easier cure. Normally, medical practitioners take a large amount of time to understand and identify the presence of cancer cells in the human body. This can lead to serious complications even to the death of the individual. Hence there is a need to identify and detect the presence of this disease very accurately and in a shorter span of time. Like every other industry, the medical industry is shifting its paradigm to automation giving excellent results having high accuracy and efficiency, which is achieved using Artificial Intelligence. There are two sets of models developed based on the numerical dataset Wisconsin and image dataset BreakHis. Machine Learning algorithms and Deep Learning algorithms were applied on the Wisconsin dataset. Meanwhile, Deep Learning models were used for analysis of the Breakhis dataset. Machine Learning models- Logistic Regression, K Neighbors, Naive Bayes, Decision tree, Random Forest and Support vector classifiers were used. Deep Learning models- normal deep learning models, Convolutional Neural Network (CNN), VGG16 &amp; VGG19 models. All the models have provided a very good accuracy ranging between 75% and 100%. Since medical research has a requirement for higher accuracy, these models can be considered and embedded into several applications. Grenze Scientific Society, 2022. Creator An entity primarily responsible for making the resource Raghuthaman A.; Jacob L. Source A related resource from which the described resource is derived 13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022, Vol-8, pp. 695-703. Publisher An entity responsible for making the resource available Grenze Scientific Society Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143340610&amp;partnerID=40&amp;md5=62622698cc37d4d6575a057dd1b31148" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143340610&amp;partnerID=40&amp;md5=62622698cc37d4d6575a057dd1b31148</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-171385793-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raghuthaman A., Christ (Deemed to be University, Pune Lavasa Campus, Maharashtra, India; Jacob L., Christ (Deemed to be University, Pune Lavasa Campus, Maharashtra, India