Anemia Selection in Pregnant Women by using Random prediction (Rp) Classification Algorithmd
Dithy M.D1, V KrishnaPriya2 

1Dithy M.D, Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, India.
2Dr. V Krishna Priya, Professor and Head-PG, School of Computing, Sri Ramakrishna College of Arts and Science, Coimbatore, India.

Manuscript received on 02 March 2019 | Revised Manuscript received on 06 March 2019 | Manuscript published on 30 July 2019 | PP: 2623-2630 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3016078219/19©BEIESP | DOI: 10.35940/ijrte.B3016.078219
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Abstract: Anemia is the global hematological disorder that occurs in pregnancy. The feature selection of unknown logical knowledge from the large dataset is capable with data mining techniques. The paper evaluates anemia features classes of Non-anemic, Mild and Severe or moderate in real time large-dimensional dataset. In the previous works, Anemia diseases can be classified in a selection of approaches, based on the Artificial Neural Networks (ANN), Gausnominal Classification and VectNeighbour classification. In these previous studies attains the proper feature selection with classification accuracy but it takes large time to predict the feature selection. So the current paper to overcome the feature selection, computational time process presents an improved Median vector feature selection (IMVFS) algorithm and new RandomPrediction (RP) classification algorithm to predict the anemia disease classes (Mild, Not anemic and Severe and moderate) based on the data mining algorithms. The results have shown that the performance of the novel method is effective compared with our previous Classification of ANN, Gausnominal and VectNeighbour classification algorithms. As the Experimental results show that proposed RandomPrediction (RP) classification with (IMVFS) feature selection methods clearly outperform than our previous methods.
Index Terms: Anemia, Data Mining, Random forest, Median Vector, Feature Selection

Scope of the Article: Classification