Comparison of Machine Learning and Deep Learning Algorithms for Classification of Breast Cancer

Puji Ayuningtyas, Rahmawati Rahmawati, Akhmad Miftahusalam

Abstract


Statistical data from the American Cancer Society which shows that breast cancer ranks first with the highest number of cases of all types of cases of malignant tumors (cancer) worldwide. through a data mining process that is used to extract information and data analysis, a classification process can be carried out to carry out further analysis of the pattern of a data. The dataset used in this study is the Breast Cancer Wisconsin (Diagnostic) Dataset obtained from UCI Machine Learning. The purpose of this study is to compare five algorithms, namely Logistic Regression, K Neighbors Classifier (KNN), Decision Tree Classifier, Deep Neural Network, Genetic Algorithm. The results showed that deep neural network algorithms and multilayer perceptron-genetic algorithms get 96% accuracy, logistic regression algorithms have 96% accuracy, then KNN with 94%, and decision tree classifier with 92%.

Keywords


Decision Tree Classifier; Deep Neural Network; Genetic Algorithm; KNN; Logistic Regression

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References


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DOI: https://doi.org/10.17509/coelite.v2i2.59717

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