Medical Disease Diagnosis Using Structuring Text
N. S. Nithya · 2014
Medical diagnosis is an important domain of research which aids to identify the occurrence of a disease. The paper proposes a novel glide path to knowledge discovery in medical systems by acquiring relevant information from the data set. This approach makes diagnosis easier. Using naive bayes, the overall speed and accuracy of the algorithm increased and extract high quality data set from an unstructured text. The primary advantage of the scheme is that it can be used to whatever sort of dataset whether it is a predefined dataset or not. Keywords-Medical diagnosis, knowledge discovery, naive bayes. I. INTRODUCTION Medical data mining focuses on various data mining techniques used particularly in medical application. Various Malaysian medical data collected and stored in Medical Data Repository. These data are used for various techniques and tasks. Among techniques used are statistical techniques, Neural Network, Rough Set Theory and Hybrid techniques. Medical data repository is a complete collection of medical data stored systematically and accessible in various formats. The data repository serves as a platform for researchers to develop new data mining techniques. The collection of data mining techniques will produce an intelligent data miner to support medical users such as medical institutions, hospitals, research centers, medical specialist and officers, medical policy makers and government. Medical data mining is one of key issues to get useful clinical knowledge from medical databases. These algorithms either rely on medical knowledge or general data mining techniques. Further, it is often the case that finding the correct subset of predictive features is an important problem in its own right. For example, physician may make a decision based on the selected features whether a dangerous surgery is necessary for treatment or not.