Comparison of ML and DL model for Breast cancer prediction

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Implementation of SVM, Decision Tree and Neural Network for Breast cancer prediction from wisconsin dataset

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Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Decision Tree, SVM an Neural Network) to develop the prediction models using a large dataset.


The objective of study is to develop an application system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) and other classification models. This will assist the doctors in diagnosis of the disease. The implement three models of machine learning techniques namely Neural Network, Decision Tree and SVM algorithm. Experimental results show that proposed methods shows the best performance in the testing data set. The patient can enter the details and get the result of breast cancer possibility as Benign or Malignant. The proposed model indicates its better ability for solving the classification problem of Breast Cancer diagnosis.


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