PREDICTIVE DATAMINING AND KNOWLEDGE DISCOVERY IN MEDICAL DATA

Abstract

newline quotBiomedical informatics is an emerging discipline that bridges two important scientific fields, namely Biology and Medicine with Computer Science. This bridging is an absolute necessity due to the vast amounts of data that are being collected in both the medical and biological fields. These data contain valuable information that awaits extraction and analysis. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making or genomic analysis. Machine learning and data mining techniques have proven to be excellent tools for knowledge extraction in clinical and genomic data has become a very important topic in scientific research. newlineOver the years, health care institutions all over the world have been collecting enormous volumes of medical data. For example, gigabytes of data are collected everyday from imaging techniques like MRI, PET, and collection of ECG or EEG signals. Huge efforts are being made by computer scientists and statisticians to design and implement algorithms and techniques for efficient storage, management, processing, and analysis of Medical database. Data mining is an emerging area of computational intelligence that offers new theories, techniques and tools for processing large volumes of data (Sriraam, Natasha and Kaur, Data mining approaches for kidney dialysis treatment, newline2006). The data mining and statistical learning techniques were used to discover consistent and useful patterns in large datasets. These techniques are used in a computational biology and bioinformatics fields. Computational biology and bioinformatics seeks to solve biological problems by combining aspects of biology,quot newline