A STUDY ON PREDICTION OF DIABETIC DISORDER USING CLASSIFICATION BASED APPROACHESJournal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.7, No. 1)
Publication Date: 2018-01-30
Authors : P.Hema; K.Palanivel;
Page : 53-60
Keywords : Data mining; Diabetic Disease; Random Forest Tree; Rep Tree; Decision Stump; Classification; WEKA;
Data mining has a considerable potential in health cooperation industry to enable health position by systematically handling data, look the composure and improve care with minimized cost. Medical professionals have passion in developing reliable prediction methodologies to recognize various diseases. Medical data mining helps to identify the health problem and get recovery quickly. The diabetic disease is a common problem found in disease most of the countries and people are suffering a lot, because of this disease. This research work focuses on the classification techniques, namely Random Forest Tree, Rep Tree, and Decision Stump which are applied to diabetic datasets to predict the possibility of the disease efficiently by analysing the relationship of diabetic data. The objective here is to study the performance of the three classification algorithm and identify the best classifier technique with good accuracy. From the Experimental results of three algorithms, Rep Tree provided best result when compared with other two algorithms. The result will help the doctors in that considered diagnosis process.
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Last modified: 2018-01-27 23:09:02