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Diagnosis of Thyroid Disorders using Decision Tree Splitting Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 44 - Number 8 |
| Year of Publication: 2012 |
| Authors: J. Jacqulin Margret, B. Lakshmipathi, S. Aswani Kumar |
10.5120/6287-8474
|
J. Jacqulin Margret, B. Lakshmipathi, S. Aswani Kumar . Diagnosis of Thyroid Disorders using Decision Tree Splitting Rules. International Journal of Computer Applications. 44, 8 ( April 2012), 43-46. DOI=10.5120/6287-8474
Abstract
Thyroid gland secretes thyroid hormones to control the body's metabolic rate. The malfunction of thyroid hormone will leads to thyroid disorders. The under-activity and over-activity of thyroid hormone causes hypothyroidism and hyperthyroidism. This paper describes the diagnosis of thyroid disorders using decision tree attribute splitting rules. Since, decision tree attempts to follow one decision, it helps to classify the data in dataset according to aforesaid disorders. This method provides five different splitting criteria for the construction of decision tree. The splitting criteria are Information Gain, Gain Ratio, Gini Index, Likelihood Ratio Chi-Squared Statistics, Distance Measure. Among, the aforementioned splitting rules three rules belong to Impurity based splitting criteria and other two are Normalized Impurity based splitting criteria. As a result, the decision tree classifies the thyroid data-set into three classes of thyroid disorders.
References
- Dr. Sahni BS, Thyroid Disorders [online]. Available : http://www. homoeopathyclinic. com/articles/diseases/tyroid. pdf
- www. wikipedia. org/thyroid
- Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques. Published by Elsevier 2006.
- V. Podgorelec, P. Kokol, B. Stiglic, and I. Rozman, Decision trees: An overview and their use in medicine. In Proceedings of Journal Medical System 2002, pages 445–463.
- http://archive. ics. uci. edu/ml/machine-learning-databases/ thyroid-disease/
- Anupam Shukla, Prabhdeep Kaur, Ritu Tiwari and R. R. Janghel, Diagnosis of Thyroid disease using Artificial Neural Network. In Proceedings of IEEE IACC 2009, pages 1016-1020.
- Lale Ozyilmaz and Tulay Yildirim, Diagnosis of Thyroid disease using Artificial Neural Network Methods. In Proceedings of ICONIP 2002, pages 2033-2036.
- Fatemeh Saiti and Mahdi Aliyari, Thyroid Disease Diagnosis based on Genetic algorithms using PNN and SVM. In Proceedings of IEEE 2009.
- C. Ordonez, Comparing association rules and decision trees for disease prediction. In Proceedings of Int. Conf. Inf. Knowl. Manage. , 2006, pages 17–24.
- Kalle Saastamoinen and Jaakko Ketola, Medical Data Classification using Logical Similarity Based Measures. In Proceedings of IEEE 2006.
- Saeed Shariati and Mahdi Motavalli Haghighi, Comparison of anfis neural network with several other anns and support vector machine for diagnosing hepatitis and thyroid diseases. In Proceedings of IEEE IACSIT 2010, pages 596-599.
- Modjtaba Rouhani and Kamran Mansouri, Comparison of several ANN architectures on the Thyroid diseases grades diagnosis. In Proceedings of IEEE IACSIT 2009, pages 526-528.
- D. Selvathi and V. S. Sharnitha, Thyroid Classification and Segmentation in Ultrasound Images Using Machine Learning Algorithms. In Proceedings of IEEE ICSCCN 2011, pages 836-841.
- Lior Rokach and Oded Maimon, Decision Trees. Tel-Aviv University, pages 165-192.
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