Biomedical Research

Machine learning techniques have been shown to support multiple medical prognoses. The purpose of this article is to compare some machine learning techniques to compare the diagnosis of breast cancer (cancerous and noncancerous) using the inputs from five supervised machine learning approaches through the different feature selections to get a correct result. Random Forests and the K-NNs model predict the most significant true positives among the five techniques. In addition, SVC and RFs models predict the most significant number of true negatives and the lowest number of false negatives. The SVC obtains the highest specificity of 96%, and the XGB obtains the lowest specificity of 92.3%. From this study, it is concluded that the Random Forests and K-NN Machine Leaning models are the most suitable models for breast cancer diagnosis with an accuracy rate greater than 95%.

Author(s): Smita Parija
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