Medical data mining by using MATLAB/SAS

Nowadays each individual and organization - business, family or institution can access a large quantity of data and information about itself and its environ¬ment. Information is scattered within different archive systems that are not connected with one another, producing an inefficient organ¬ization of the data. Two developments could help to overcome these problems. First, software and hardware continually, offer more power at lower cost, allowing organizations to collect and organize data in structures that give easier access and transfer. Second, methodological research, particularly in the field of computing and statistics, has recently led to the development of flexible and scalable procedures that can be used to analyze large data stores. These two developments have meant that data mining is rapidly spreading through many businesses as an important intelligence tool for backing up decisions.In this thesis data mining process has been expressed. Subjects obtained as how to process the data, which stages to migrate, which model would be available for the data. Statistical issues supporting data mining process has been analyzed. Then issue of how data mining in medical area which is one of the usage areas of data mining is obtained. Similarity between normal data mining process and medical data mining process has been analyzed. Problems faced in medical data mining process are obtained and methods to solve these problems are searched. Appliance is done on clustering and logistics regression, one of the statistical methods that support data mining. Clustering appliance is held on EEG data derived from the biophysics Program University of Dokuz Eylül. Appliance on logistics regressions has been made by data derived from patients of IVF department of the a private hospital. These appliances have been resulted as data mining is incredibly important in medical area and that is so beneficial in solution of the huge amounts.