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Performance Analysis of Machine Learning Algorithms for Thyroid Disease

  • Research Article-Electrical Engineering
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Abstract

Thyroid disease arises from an anomalous growth of thyroid tissue at the verge of the thyroid gland. Thyroid disorderliness normally ensues when this gland releases abnormal amounts of hormones where hypothyroidism (inactive thyroid gland) and hyperthyroidism (hyperactive thyroid gland) are the two main types of thyroid disorder. This study proposes the use of efficient classifiers by using machine learning algorithms in terms of accuracy and other performance evaluation metrics to detect and diagnose thyroid disease. This research presents an extensive analysis of different classifiers which are K-nearest neighbor (KNN), Naïve Bayes, support vector machine, decision tree and logistic regression implemented with or without feature selection techniques. Thyroid data were taken from DHQ Teaching Hospital, Dera Ghazi Khan, Pakistan. Thyroid dataset was unique and different from other existing studies because it included three additional features which were pulse rate, body mass index and blood pressure. Experiment was based on three iterations; the first iteration of the experiment did not employ feature selection while the second and third were with L1-, L2-based feature selection technique. Evaluation and analysis of the experiment have been done which consisted of many factors such as accuracy, precision and receiver operating curve with area under curve. The result indicated that classifiers which involved L1-based feature selection achieved an overall higher accuracy (Naive Bayes 100%, logistic regression 100% and KNN 97.84%) compared to without feature selection and L2-based feature selection technique.

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Abbreviations

k :

Number of neighboring elements

L1 :

L1-norm

L2 :

L2-norm

a, b :

Feature vectors

d :

Distance

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Acknowledgements

I would like to extend my sincere gratitude to Dr. Abid Hussain, Dr. Zarnab Lashari and Dr. Aimen Javed for aiding in gathering Thyroid Data and contributing continuous support. I am thankful to them for their invaluable guidance.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Hafiz Abbad Ur Rehman & Chyi-Yeu Lin

  2. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Zohaib Mushtaq & Shun-Feng Su

Authors
  1. Hafiz Abbad Ur Rehman
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  2. Chyi-Yeu Lin
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  3. Zohaib Mushtaq
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  4. Shun-Feng Su
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Correspondence to Hafiz Abbad Ur Rehman.

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Abbad Ur Rehman, H., Lin, CY., Mushtaq, Z. et al. Performance Analysis of Machine Learning Algorithms for Thyroid Disease. Arab J Sci Eng 46, 9437–9449 (2021). https://doi.org/10.1007/s13369-020-05206-x

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  • DOI: https://doi.org/10.1007/s13369-020-05206-x

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