Smita Parija

Publications

• Research Article   
  Prediction of breast cancer using tools of machine learning techniques
  Author(s): Madhumita Pal, Smita Parija* and Ganapati Panda
  
  Objective: 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 non-cancerous) using the inputs from five supervised machine learning approaches through the different feature selections to get a correct result. Materials and methods: This study included 683 cases of breast cancer, four hundred and forty-four being benign and two hundred and thirty-nine malignant; the studied data were taken from the UCI machine learning repository. Ten models for machine learning were evaluated and only five were selected from the correlation matrix (SVC, logistic regression, random forests, XGBoost or K-NNs). Results: Random forests and the K-NNs model predict the most significant true positives among the five techniques... Read More»
  

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