THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

Authors

  • Kumawuese Jennifer Kurugh
  • Muhammad Aminu Ahmad Kaduna State University
  • Awwal Ahmad Babajo

DOI:

https://doi.org/10.33003/fjs-2020-0404-514

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

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Published

2021-01-01

How to Cite

Kurugh, K. J. ., Ahmad, M. A., & Babajo, A. A. (2021). THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS. FUDMA JOURNAL OF SCIENCES, 4(4), 309 - 315. https://doi.org/10.33003/fjs-2020-0404-514