A Comparative study of machine learning algorithms on thyroid disease prediction

  • Authors

    • Shaik Razia
    • P SwathiPrathyusha
    • N Vamsi Krishna
    • N Sathya Sumana
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10432
  • SVM, Multiple Linear Regression, Naïve Bayes, Decision Trees.

  • Thyroid illness is a medicinal state that influences the functionality of the thyroid organ that is thyroid gland [1](Guyton, 2011).The indications of thyroid ailment differ basing upon the type. There are four most common varieties: hypothyroidism (low capacity) which is caused due to the insufficiency of the thyroid hormones; hyperthyroidism (high capacity) which is caused due to the existence of the thyroid hormones more than just sufficient, basic variations from the norm, most normally an augmentation of the thyroid organ; and tumors which can be benign or can cause cancer. It is additionally conceivable to have irregular thyroid capacity tests with no clinical side effects [2](Bauer & al, 2013).In this study a comparative thyroid disease diagnosis were performed by using Machine learning techniques that is Support Vector Machine (SVM), Multiple Linear Regression, Naïve Bayes, Decision Trees. For this purpose, thyroid disease dataset gathered from the UCI machine learning database was used.

  • References

    1. [1] C., Guyton, Arthur. Guyton and Hall textbook of medical physiology, OCLC 4343l9356

      [2] Bauer, DC; et al. (2Ol3). Path physiology of Disease: An Introduction to Clinical Medicine, Seventh Edition, New York, NY: McGraw-Hill – via Access Medicine.

      [3] http://sci2s.ugr.es/keel/dataset.php?cod=67

      [4] Keleş A, Keleş A. Estdd: Expert system for thyroid dis-eases diagnosis. Expert Systems with Applications. 2OO8; 34:242–6.

      [5] Ozyilmaz L, Yildirim T. Diagnosis of thyroid diseaseusing artificial neural network methods. Proceedings of the 9th International Conference on Neural Information Processing, 2OO2. ICONIP’O2; 2OO2. p. 2O33–6.

      [6] Temurtas F. A comparative study on thyroid diseasediagnosis using neural networks. Expert Systems withApplications. 2OO9; 36:944–9.

      [7] Hasan Makas, Nejat Yumusak (2ol3) A Comprehensive Study On Thyroid Diagnosis By Neural Networks And Swarm Intelligence. International Conference On Electronics, Computer And Computation (Icecco):L8o-L84.Doi:Lo.Llo9/Icecco.2ol3.67l8258

      [8] V. Prasad, T. Srinivasa Rao, M. Surendra Prasad Babu, Thyroid Disease Diagnosis Via Hybrid Architecture Composing Rough Data Sets Theory And Machine Learning Algorithms. Soft Computing 2o(3):Ll79-Ll89 2ol5.

      [9] Li-Na Li, Ji-Hong Ouyang, Hui-Ling Chen, Da-You Liu, A Computer Aided Diagnosis System For Thyroid Disease Using Extreme Learning Machine. Journal Of Medical Systems 36(5):3327-3337 2ol2

      [10] Shaik.Razia, M.R.Narasingarao Published “A Neuro Computing Frame Work For Thyroid Disease Diagnosis Using Machine Learning Techniquesâ€, Vol.95. No.9. Pages L996-2oo5, Issn: L992-8645.

      [11] P. Gopi Krishna, K. Sreenivasa Ravi “Designing A Multipurpose Reconfigurable Wireless Node For Broadcasting And Unicasting In Rereal Time Applications†In International Journal Of Pure And Applied Mathematics (Ijpam). Volume Ll5 No. 8 2ol7, 5o5-5lo.

      [12] P Gopi Krishna, K Sreenivasa Ravi “Implementation OfMqtt Protocol On Low Resourced Embedded Network†In International Journal Of Pure And Applied Mathematics (Ijpam). Volume Ll6 No. 6 2ol7, L6l-L66.

      [13] Dr. Seetaiah Kilaru, Hari Kishore K, Sravani T, Anvesh Chowdary L, Balaji T “Review And Analysis Of Promising Technologies With Respect To Fifth Generation Networksâ€, 2ol4 First International Conference On Networks & Soft Computing, Issn:978-L-4799-34867/L4,Pp.27o-273,August2ol4.

      [14] N.Prathima, K.Hari Kishore, “Design Of A Low Power And High Performance Digital Multiplier Using A Novel 8t Adderâ€, International Journal Of Engineering Research And Applications, Issn: 2248-9622, Vol. 3, Issue.L, Jan-Feb., 2ol3.

      [15] T. Padmapriya And V. Saminadan, “Improving Throughput For Downlink Multi User Mimo-Lte Advanced Networks Using Sinr Approximation And Hierarchical Csi Feedbackâ€, International Journal Of Mobile Design Network And Innovation- Inderscience Publisher, Issn : L744-285o Vol. 6, No.L, Pp. L4-23, May 2ol5.

      [16] S.V.Manikanthan And K.Srividhya “An Android Based Secure Access Control Using Arm And Cloud Computing", Published In: Electronics And Communication Systems (Icecs), 2ol5 2nd International Conference On 26-27 Feb. 2015,Publisher: Ieee,Doi: Lo.Llo9/Ecs.2ol5.7l24833.

      [17] M. Rajesh, Manikanthan, “Annoyed Realm Outlook Taxonomy Using Twin Transfer Learningâ€, International Journal Of Pure And Applied Mathematics, IssnNo:L3l4-3395, Vol-Ll6, No. 2l, Oct 2ol7.

      [18] [18] K.Srikar, M.Akhil, V.Krishna Reddy “Execution Of Cloud Scheduling Algorithms†International Innovative Research Journal Of Engineering And Technology Issn No: 2456-L983.Volume 2, Issue 4 June 2ol7.

      [19] Meherwar Fatima, M. P. (2ol7). Survey Of Machine Learning Algorithms For Disease Diagnostic. Journal Of Intelligent Learning Systems And Applications, L-L6.

      [20] Jian, A. (2ol5). Machine Learning Techniques For Medical Diagnosis. Icstat.

      [21] Alic, B. (2ol7). Machine Learning Techniques For Classification Of Diabetes And Cardiovascular Diseases. Mediterranean Conference On Embedded Computing.

      [22] Shaik Razia, M.R.Narasingarao, Polaiah Bojja Published “Development And Analysis Of Support Vector Machine Techniques For Early Prediction Of Breast Cancer And Thyroid†In Scopus Indexed Journal Jardcs (Journal Of Advanced Research In Dynamical And Control Systems, 2017.Vol.9.Sp.Issue:6 Issn: 1943-023x Page No: 869-878).

      [23] Shaik Razia Published“A Review On Disease Diagnosis Using Machine Learning Techniquesâ€International Journal Of Pure And Applied Mathematics, 2017, Volume 117, No. 16 2017, 79-85, Issn: 1311-8080.

      [24] Shaik Razia, M.R.Narasingarao, Polaiah Bojja Published “The Analysis Of Data Representation Techniques For Early Prediction Of Breast Cancer†International Journal Of Pure And Applied Mathematics, 2017, Volume-115, Issue: 6, Issn: 1311-8080, Issn: 1314-3395 Page No: 177-183.

      [25] Shaik Razia, M.R.Narasingarao Published “Machine Learning Techniques For Thyroid Disease Diagnosis - A Reviewâ€Indian Journal Of Science And Technology, Issn: 09746846, Volume-9, Issue 28, July 2016.

      [26] Shaik Razia, M.R.Narasingarao,G R Sridhar Published “A Decision Support System For Prediction Of Thyroid Disease- A Comparison Of Multilayer Perception Neural Network And Radial Basis Function Neural Network†Journal Of Theoretical And Applied Information Technology, 31st October 2015. Vol.80. No.3, Issn: 1992-8645.

      [27] Meka Bharadwaj, Hari Kishore "Enhanced Launch-Off-Capture Testing Using Bist Designs†Journal Of Engineering And Applied Sciences, Issn No: 1816-949x, Vol No.12, Issue No.3, Page: 636-643, April 2017.

      [28] P Bala Gopal, K Hari Kishore, R.R Kalyan Venkatesh, P Harinath Mandalapu“An Fpga Implementation Of On Chip Uart Testing With Bist Techniquesâ€, International Journal Of Applied Engineering Research, Issn 0973-4562, Volume 10, Number 14 , Pp. 34047-34051, August 2015.

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  • How to Cite

    Razia, S., SwathiPrathyusha, P., Vamsi Krishna, N., & Sathya Sumana, N. (2018). A Comparative study of machine learning algorithms on thyroid disease prediction. International Journal of Engineering & Technology, 7(2.8), 315-319. https://doi.org/10.14419/ijet.v7i2.8.10432