MICROARRAY USING ADVANCED REGRESSION FOR FIXING LOST DATA

Data missing is the more complicated problem in now a day, especially in hospitals. Most of the hospitals are using client server technology for data transferring inside the hospital. While transferring huge database from one location to another location else in case of data migration data loss may occur. At the time some values from the table or from database may disappear. These problems are said to be as data missing. In order to find out the missing values, sometimes prediction may used to fill the data. Prediction should be more accurate. So here we are implementing a multidimensional array model with modified advanced regression. From the data set, an operational database will be created for the cancer patients and a database for normal patients. This database will be unique and different types of sample data are available. The modified advanced regression will compare the existing spatial database with the normal database from the input database. So the result will be obtained from the dataset. Whether the patient will affect from cancer or not, also their infection ration percentage can be find out, along with the missing values in the database during the time of data migration. An improved advanced regression was introduced, to find the missing values in the dataset. BPCA obtains the lowest normalized rootmean- square error on 82.14% of all missing rates. Here we are proposing for 95.79 % of accuracy in the improvised algorithm.