Anemia Screening in Pregnant Women by Using Vect Neighbour Classification Algorithm

M.D. Dithy and Dr.V. Krishna Priya

Anemia is the common hematologic uncertainty that happens in pregnancy. Extraction of hidden analytical information from the large dataset is possible with the data mining process. This paper presents an extract the relevant features and prediction of classes (Non-anemic, Mild and Severe or moderate) in Anemia high-dimensional data set. In existing work, Anemia can be classified in a variety of ways, based on the Artificial Neural Networks (ANN), Gausnominal Classification, etc. In these studies, the relevant features are previously defined and predict the classification accuracy this paper presents an examination of the prediction and classification of anemia in patients using data mining techniques without previously defined. This paper presents an enhanced Rough set based Fuzzy threshold (RFT) feature selection process and new Vect Neighbour classification algorithm to forecast the anemia disease classes (Mild, Not anemic and Severe and moderate) based on the data mining techniques. The results showed that the performance of the new scheme is effective compared with other Classification of ANN and Gausnominal classification algorithms. The Experimental results show that proposed Vect Neighbour classification with RFT) feature selection methods clearly outperform than existing methods.

Volume 11 | 04-Special Issue

Pages: 1894-1905