Performance of Logistic Model Tree to Foresee an Occurrence of Thyroid Disease

  • Sharvan Kumar Garg, Manoj Kapil


Premature forecasting of thyroid is extremely imperative to save an individual years and take appropriate steps to control the ailment. Decision Tree algorithms have been effectively useful in a variety of fields particularly in medicinal discipline. This manuscript investigates the premature forecasting of thyroid by means of Logistic Model Tree (LMT) algorithm. In this manuscript, we build up a Thyroid prediction model that can aid medical experts in envisaging Thyroid condition supported on the medicinal data of patients. At the outset, we have chosen 21 imperative medicinal attributes viz., age, sex, TSH, T3, TT4, T4U, FTI, Pregnant, Thyroid_surgery, I131_treatment , etc., in addition to one target class. Secondly, we build up a prediction model using Logistic Model Tree algorithm classifier for classifying Thyroid based on these clinical attributes. Lastly, the precision of Logistic Model Tree approach proves to be 99.514% accurate. Outcome acquired illustrates that Thyroid_surgery, Goitre, TSH, T3 and FTI are the foremost predictive attributes which provides enhanced classification in opposition to the supplementary attributes.


Keywords: LMT, Logistic Model Tree, Thyroid, Decision Tree, Classifier, Data Mining

How to Cite
Sharvan Kumar Garg, Manoj Kapil. (2020). Performance of Logistic Model Tree to Foresee an Occurrence of Thyroid Disease. International Journal of Advanced Science and Technology, 29(3), 9584 - 9595. Retrieved from