Decision Tree Ensemble Techniques To Predict Thyroid Disease
Dhyan Chandra Yadav1 , Saurabh Pal2
1Dhyan Chandra Yadav, Department of Computer Applications, Veer Bahadur Singh Purvanchal University, Jaunpur, India.
2Saurabh Pal, Department of Computer Applications, Veer Bahadur Singh Purvanchal University, Jaunpur, India.
Manuscript received on 07 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 8242-8246 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6727098319/19©BEIESP | DOI: 10.35940/ijrte.C6727.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Decision tree provides help in making decision for very complex and large dataset. Decision tree techniques are used for gathering knowledge. Classification tree algorithms predict the experimental values of women thyroid dataset. The objective of this research paper observation is to determine hyperthyroidism, hypothyroidism and euthyroidism participation in hormones can be good predictor of the final result of laboratories and to examination whether the propose ensemble approach can be similar accuracy to other single classification algorithm. In the proposed experiment real data from 499 thyroid patients were used classifications algorithms in predicting whether thyroid detected or not detected on the basis of T3, T4 and TSH experimental values. The results show that the expectation of maximization classification tree algorithms in those of the best classification algorithm especially when using only a group of selected attributes. Finally we predict batch size, tree confidential factor, min number of observation, num folds, seed, accuracy and time build model with different classes of thyroid sickness. Different classification algorithms are analyzed using thyroid dataset. The results obtained by individual classification algorithms like J48, Random Tree and Hoeffding gives accuracy 99.12%, 97.59% and 92.37 respectively. Then we developed a new ensemble method and apply again on the same dataset, which gives a better accuracy of 99.2% and sensitivity of 99.36%. This new proposed ensemble method can be used for better classification of thyroid patients.
Keywords: J48, Random Tree, Hoeffding, Prediction, T3, T4, TSH, Hypothyroidism, Hyperthyroidism, Euthyroidism and Ensemble Model
Scope of the Article: Regression and Prediction