Inductive Machine Learning and Feature Selection for Knowledge Extraction from Medical Data: Detection of Breast Lesions in MRI

This paper presents an approach to the problem of breast cancer diagnosis through the data analysis of magnetic mammography observations (MRi Data), developing corresponding hybrid classification models of patient cases into specific classes (e.g. Benign and Malignant). The aim of this work is the contribution of machine learning to the diagnostic process of breast cancer, offering a supportive intelligent tool that can be used by expert doctors as a medical decision-making aiding tool. Data were collected in collaboration with expert doctors and consist of 77 patient cases. The development of the presented classification models is a combination of inductive decision trees, clustering and feature selection techniques. Specifically, nine (9) different classification models were developed and evaluated by using statistical criteria, medical expert knowledge and where possible, using the Chi-Square statistical test. The performance achieved is considered encouraging for application in real-world practice, while further research is underway for associating MR imaging data with data from invasive examinations (biopsies).