Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis

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REZAEI MOHSEN; Jafari Nazanin Zahra; Ghaffarian Hossein; Khosravi Farmad Masoud; ZABBAH IMAN; DEHGHAN PARVANEH

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Journal: Payavard Salamat Year:2020 | Volume:13 | Issue:5 Page(s): 345-358

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Title

Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis

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Keywords

Thyroid DiseaseQ1

Fuzzy ClusteringQ1

Artificial Neural NetworksQ1

Neural FuzzyQ1

Supervised Learning NetworksQ1

Abstract

Background and Aim: Timely diagnosis and treatment of abnormal thyroid function can reduce the mortality associated with this disease. However, lack of timely diagnosis will have irreversible complications for the patient. Using data mining techniques, the aim of this study is to determine the status of the thyroid gland in terms of normality, hyperthyroidism or hypothyroidism. Materials and Methods: Using supervised and unsupervised methods after data preprocessing, predictive modeling was performed to classify Thyroid Disease. This is an analytical study and its dataset contains 215 independent records based on 5 continuous features retrieved from the UCI machine learning data reference. Results: In supervised method, multilayer perception(MLP), learning vector quantization(LVQ), and fuzzy neural network(FNN) were used; and in unsupervised method, Fuzzy Clustering was employed. Besides, these precision figures(0. 055, 0. 274, 0. 012 and 1. 031) were obtained by root mean square error(RMSE) method, respectively. Conclusion: Reducing the diagnosis error of Thyroid Disease was one of the goals of researchers. Using data mining techniques can help reduce this error. In this study, Thyroid Disease was diagnosed by different pattern recognition methods. The results show that the fuzzy neural network(FNN) has the least error rate and the highest accuracy.

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APA: Copy

REZAEI, MOHSEN, Jafari, Nazanin Zahra, Ghaffarian, Hossein, Khosravi Farmad, Masoud, ZABBAH, IMAN, & DEHGHAN, PARVANEH. (2020). Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis. PAYAVARD-SALAMAT, 13(5 ), 345-358. SID. https://sid.ir/paper/364611/en

Vancouver: Copy

REZAEI MOHSEN, Jafari Nazanin Zahra, Ghaffarian Hossein, Khosravi Farmad Masoud, ZABBAH IMAN, DEHGHAN PARVANEH. Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis. PAYAVARD-SALAMAT[Internet]. 2020;13(5 ):345-358. Available from: https://sid.ir/paper/364611/en

IEEE: Copy

MOHSEN REZAEI, Nazanin Zahra Jafari, Hossein Ghaffarian, Masoud Khosravi Farmad, IMAN ZABBAH, and PARVANEH DEHGHAN, “Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis,” PAYAVARD-SALAMAT, vol. 13, no. 5 , pp. 345–358, 2020, [Online]. Available: https://sid.ir/paper/364611/en

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