%0 Journal Article %T To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques %J Asian Pacific Journal of Cancer Prevention %I West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. %Z 1513-7368 %A Yadav, Dhyan Chandra %A Pal, Saurabh %D 2019 %\ 04/01/2019 %V 20 %N 4 %P 1275-1281 %! To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques %K Meta Classifier algorithms: Boosting %K Bagging %K Ensemble-I %K Ensemble-II %R 10.31557/APJCP.2019.20.4.1275 %X Objective: The main objective of this paper is to easily identify thyroid symptom for treatment. Methods: Inthis paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble-II both are machine learning techniques. Ensemble-I generated from decision tree, over fitting and neural networkand Ensemble-II generated from combinations of Bagging and Boosting techniques. Finally proposed experiment isconducted by Ensemble-I vs. Ensemble-II. Results: In the entire experimental setup find an ensemble –II generatedmodel is the higher compare to other ensemble-I model. In each experiment observe and compare the value of all theperformance of ROC, MAE, RMSE, RAE and RRSE. Stacking (ensemble-I) ensemble model estimate the weightsfor input with output model by thyroid dataset. After the measurement find out the results ROC=(98.80), MAE=(0.89), 6RMSE=(0.21), RAE= (52.78), RRSE=(83.71)and in the ensemble-II observe thyroid dataset and measure allperformance of the model ROC=(98.79), MAE= (0.31), RMSE=(0.05) and RAE= (35.89) and RRSE=(52.67). Finallyconcluded that (Bagging+ Boosting) ensemble-II model is the best compare to other. %U https://journal.waocp.org/article_85939_7eb7452f0a9d81750257710a54bb244f.pdf