THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

Authors

Keywords:

Breast cancer, machine learning algorithms, classification

Abstract

Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets were used; Wisconsin Breast Cancer (WBC) Original, Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC). The results show that datasets affect the performance of machine learning classifiers. Also, the machine learning classifiers have different performances with a given breast cancer dataset

Dimensions

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Published

13-06-2021

How to Cite

THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS. (2021). FUDMA JOURNAL OF SCIENCES, 4(4), 309-315. https://doi.org/10.33003/fjs-2020-0404-487

Issue

Vol. 4 No. 4 (2020): FUDMA Journal of Sciences - Vol. 4 No. 4

Section

Research Articles
Copyright & Licensing

How to Cite

THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS. (2021). FUDMA JOURNAL OF SCIENCES, 4(4), 309-315. https://doi.org/10.33003/fjs-2020-0404-487

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