Treatment of thyroid disease through machine learning predictive model

Tanvir Singh; Ajay Kumar Sahu; Shivani Dubey; Mahendra Prasad Sharma; Shanu Verma; Chaman Kumar

doi:10.53730/ijhs.v6nS8.12813

Authors

Tanvir Singh
ulab12345@gmail.com
IIMT College of Engineering, Greater Noida India, Assistant Professor, Department of MCA
Ajay Kumar Sahu Greater Noida Institute of Technology (GNIOT), Greater Noida India, Associate Professor, Department of IT
Shivani Dubey Greater Noida Institute of Technology (GNIOT), Greater Noida India, Associate Professor, Department of AI-ML & IOT
Mahendra Prasad Sharma IIMT College of Engineering, Greater Noida India, Associate Professor, Department of IT
Shanu Verma Pranveer Singh Institute of Technology, Kanpur India, Assistant Professor, Department of CSE
Chaman Kumar IIMT College Of Engineering, Greater Noida India, Assistant Professor, Department of IT

Keywords:

thyroid disease, machine learning algorithms, predictive models

Abstract

The thyroid seems to be an part of the endocrine system that is placed toward the front of neck and produces thyroxine, which are essential for our overall health. If it fails, thyroid hormone production will either be insufficient or excessive. Machine learning techniques and data mining are critical in processing large amounts of data, particularly in the health care system, where there has been a massive amount of information and data need to be managed. In our research on thyroid disease, we used machine learning approaches. In our study, we used statistics from patients, a few of which has hyperactive thyroid glands moreover those have hypothyroidisms; therefore, overall algorithms were used. These study aims to divide this disease in few categories like as hypothyroidism, regular and hyperthyroidism. Support vector machine include KNN, naive-bayes, logistic regressions, decision tree, random forest, discriminant function analysis, and multilayer perceptron (MLP). To the thyroid diseases classification.

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References

AKGÜL, Göksu, et al. "Hipotiroidi Hastalığı Teşhisinde Sınıflandırma Algoritmalarının Kullanımı." Bilişim Teknolojileri Dergisi 13.3 (2020): 255-268.

Aswad, Salma Abdullah, and Emrullah Sonuç. "Classification of VPN Network Traffic Flow UsingTime Related Features on Apache Spark." 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2020.

Aytürk Keleş and Keleş, Ali. "ESTDD: Expert system for thyroid diseases diagnosis." International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 11 | Nov -2017 34.1 (2017):242- 246

Azar, a.T, Hassanien, A.E. and Kim, T. Expert system based on neural fuzzy rules for thyroid disease diagnosis, Computer Science, Artificial Intelligence, arXiv:1403.0522, Pp. 1-12,2012.

Banu, G. Rasitha. "A Role of decision Tree classification data Mining Technique in Diagnosing Thyroiddisease." International Journal of Computer Sciences and Engineering 4.11 (2016): 64-70.

Banu, G. Rasitha. "A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease." International Journal of Computer Sciences and Engineering 4.11 (2016): 64-70.

Begum, Amina, and A. Parkavi. "Prediction of thyroid disease using data mining techniques." 20195th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2019.

Bolotnikova, Anastasia, Hasan Demirel, and Gholamreza Anbarjafari. "Real-time ensemble based face recognition system for NAO humanoids using local binary pattern." Analog Integrated Circuits and Signal Processing 92, no. 3 (2017): 467-475.

C. Fan, F. Xiao, Z. Li, J. Wang. Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review. Energy Build. 2018, 159, 296–308.

Chandel, Khushboo, et al. "A comparative study on thyroid disease detection using K-nearest neighbour and Naive Bayes classification techniques." CSI transactions on ICT 4.2-4 (2016): 313-319.

Chandio, Jamil Ahmed, et al. "TDV: Intelligent system for thyroid disease visualization." 2016International Conference on Computing, Electronic and Electrical Engineering (ICE Cube). IEEE, 2016.

Chaurasia, Vikas, Saurabh Pal, and B. B. Tiwari. "Prediction of benign and malignant breast cancerusing data mining techniques." Journal of Algorithms & Computational Technology 12.2 (2018): 119-126.

D. Mora, G. Fajilla, M. Austin, D. Simone. Occupancy patterns obtained by heuristic approaches: Cluster analysis and logical flowcharts. A case study in a university office. Energy Build. 2019, 186, 147–168

Dasarathy B. Nearest neighbor pattern classification techniques. Silver Spring, MD: IEEE Computer Society Press; 1991.

Dr. Srinivasan B, Pavya K “Diagnosis of Thyroid Disease: A Study” International Research Journal of Engineering and Technology Volume: 03 Issue: 11 | Nov – 2016

Dreiseitl, Stephan, and Lucila Ohno-Machado. "Logistic regression and artificial neural network classification models: a methodology review." Journal of biomedical informatics 35.5-6 (2002): 352-359.

Gupta, Puneet, Vijay Kumar Sharma, Naman Mittal, Raghav Bansal, and Himanshu Gupta. "Ai Enabled Virtual Environment Simulator." (2020).

Heuck, "World Health Organization," 2000. [Online]. Available: https://www.who.int/.

Iqbal, Md, Vimal Kumar, and Vijay Kumar Sharma. "Krishi Portal: Web Based Farmer Help Assistance." International Journal of Advanced Science and Technology 29 (2007): 4783-4786.

Keles, A. ESTDD: Expert system for thyroid diseases diagnosis, Expert Syst Appl., Vol. 34, No.1,Pp.242–246, 2008.

Khushboo Taneja, Parveen Sehgal, Prerana “Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network” International Journal of Research in Management, Science & Technology (E-ISSN:2321- 3264) Vol. 3, No. 2, April 2016

Kouroua, K., Exarchosa, T.P. Exarchosa, K.P., Karamouzisc, M.V. andFotiadisa, D.I. (2015) Machinelearning applications in cancer prognosis and prediction, Computational and Structural BiotechnologyJournal, Vol. 13, Pp.8–17.

Ripley B. Pattern recognition and neural networks. Cambridge: Cambridge University Press; 1996.

Sharma, Vijay Kumar, Vimal Kumar, Md Iqbal, Sachin Tawara, and Vishal Jayaswal. "Virtual Mouse Control Using Hand Class Gesture."

Shukla, A. & Kaur, P. (2009). Diagnosis of thyroid disorders using artificial neural networks, IEEE International Advance computing Conference (IACC 2009)– Patiala, India, pp 1016-1020.

Sindhya, Mrs K. "EFFECTIVE PREDICTION OF HYPOTHYROID USING VARIOUS DATAMINING TECHNIQUES."

Tiwari, Durgesh, and Vijay Kumar Sharma. "A Review on Conventional and Lightweight Security Techniques in Mobile and IoT Devices."

Travis B Murdoch and Allan S Detsky. The inevitable application of big data to health care. Jama,309(13):1351–1352, 2013.

Tyagi, Vidhi, Shivam Arora, Sattyam Gupta, Vijay Kr Sharma, and Vimal Kumar. "Architecture of an IoT-based Women Safety System." Architecture 29, no. 5 (2020): 3670-3676.

Umar Sidiq, Dr, Syed Mutahar Aaqib, and Rafi Ahmad Khan. "Diagnosis of various thyroid ailments using data mining classification techniques." Int J Sci Res Coput Sci Inf Technol 5 (2019): 131-6.

V. Cerqueira, L. Torgo, M. Mozetic. Evaluating time series forecasting models: An empirical study on performance estimation methods. Mach. Learn. 2020, 109, 1997–2028.

VijiyaKumar, K., et al. "Random Forest Algorithm for the Prediction of Diabetes." 2019 IEEEInternational Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2019.

W. Kleiminger, C. Beckel, T. Staake, S. Santini. Occupancy Detection from Electricity Consumption Data. In Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, Rome, Italy, 14–15 November 2013; pp. 1–8.