The Electronic Journal of Knowledge Management publishes original articles on topics relevant to studying, implementing, measuring and managing knowledge management and intellectual capital.

For general enquiries email
Click here to see other Scholarly Electronic Journals published by API
For a range of research text books on this and complimentary topics visit the Academic Bookshop

Information about the European Conference on Knowledge Management (ECKM) is available here.

For info on the International Conference on Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM), click here
Information about the European Conference on Intangibles and Intellectual Capital (ECIIC) is available here

Journal Article

Mining Knowledge of the Patient Record: The Bayesian Classification to Predict and Detect Anomalies in Breast CancerŽ  pp128-139

Souad Demigha

© Aug 2016 Volume 14 Issue 3, Editor: Vincent Ribiere, pp113 - 190

Look inside Download PDF (free)


Abstract: knowledge management, data mining, and text mining techniques have been adopted in various successful biomedical applications in recent years. Data Mining (DM) is the most important subfields in knowledge management (KM). It has been proven that data mining can enhance the KM process with better knowledge. In this paper, we investigate the application of DM techniques for mining knowledge of the patient record. The patient record represents documents of the patients examinations and treatments. Data Mining is the process of miningŽ or extracting information from a data set and transform it into an understandable structure for further use. We propose a methodology for mining medical knowledge based on the Bayesian Classification to predict and detect anomalies in breast cancer. We use the Naïve Bayes Algorithm to develop this methodology. We illustrate the knowledge mining process by real examples of medical field. We investigate through these illustrations how knowledge is better mined and thus, reused when applying concepts and techniques of Data Mining. On the other hand, we investigate the potential contribution of the Naive Bayesian Classification methodology as a reliable support in computer‑aided diagnosis of such events, using the well‑known Wisconsin Prognostic Breast Cancer dataset. Finally, we will demonstrate the suitability and ability of the Naive Bayes methodology in Classification/Prediction problems in breast cancer.


Keywords: Keywords: Patient Record, Data Mining, Bayesian Classification, Naïve Bayes Algorithm, Breast cancer prediction


Share |