TITLE:
Performance Improvement with Combining Multiple Approaches to Diagnosis of Thyroid Cancer
AUTHORS:
Ahmet Akbaş, Uğur Turhal, Sebahattin Babur, Cafer Avci
KEYWORDS:
Thyroid Cancer; Classification; WEKA
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
ABSTRACT: There are a lot of diseases
that carry death risk when these diseases are infected to human body, if early
measures are not taken. Thyroid cancer is one of them. In USA, number of
thyroid cancer cases resulted in death in only 2013 shows necessity of early
fight with this disease. This study aims performance improvement in diagnosis
of thyroid cancer with machine learning techniques. Study consists of 3 phases.
In the first phase, BayesNet, NaiveBayes, SMO, Ibk and Random Forest
classifiers have been trained with thyroid cancer train dataset. In the second
phase, trained classifiers have been tested with thyroid cancer test dataset
and the obtained performance results have been compared. In the third and last
phase, approaches named above have been integrated to algorithm AdaboostMI to
show difference between of ensemble classifiers from conventional individual
classifiers and first two phases have been repeated. With using ensemble
approaches performance improvement has been achieved in diagnosis of thyroid cancer.
Also, kappa, accuracy and MCC values obtained from these classifier models have
been explained in tables and effects on diagnosis of the disease have been
shown with ROC graphics. All of these operations have been carried out with
WEKA data mining program.