Accurate diagnosis of breast cancer is very beneficial as breast cancer is the second-leading cause of cancer death in women after lung cancer in the US. This study compares two machine learning approaches to diagnose breast cancer using a publicly available dataset, which comprises of features computed from a digitized image of a fine needle aspirate (FNA). We employ two different machine learning techniques, namely Naïve Bayes and Random Forest to measure the accuracy of the diagnosis. Using 569 patient's information and 31 features, the above three machine learning classifiers are implemented. According to the findings, the Random Forest classifier performed better than the Naïve Bayes method by reaching a 97.82% of accuracy. Furthermore, classification accuracy can be improved with the appropriate selection of the feature selection technique. Furthermore, this section explains the feature selection technique used in the study. The analysis procedure is discussed, and the dataset and the performance indicators are described.
"A Comparison Between Naïve Bayes and Random Forest to Predict Breast Cancer,"
International Journal of Undergraduate Research and Creative Activities: Vol. 12:
1, Article 10.
Available at: https://digitalcommons.cwu.edu/ijurca/vol12/iss1/10