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A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients

Surekha Samsani *, srinivasa rao**

* Department of Computer Science and Engineering, UCEK(A), Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

** Department of Information Technology, UCEV(A), Jawaharlal Nehru Technological University Kakinada, Andra Pradesh, India.

Periodicity:December - February'2019
DOI : https://doi.org/10.26634/jpr.5.4.15947

Abstract

Diabetes Mellitus (DM) and Thyroid are the major coexistent autoimmune disorders affecting people globally. Due to prolonged chronic mental stress in the modern lifestyle, Thyroid disorder is affecting all age groups and people with Thyroid disorder have an increased risk of developing DM complications. Because, abnormal Thyroid dysfunction can have dreadful effects on blood glucose control and can affect the course of DM. This paper proposes a Medical Expert system to assist clinicians in predicting the prevalence of developing autoimmune DM more precisely in patients suffering from Thyroid and further helps to investigate better in the line of improving public health. In this work, Fuzzy logic based inference system and unsupervised machine learning algorithms are used to discover associations and dependencies between Thyroid and DM. The inferred knowledge base is used to design Fuzzy based Expert system. To develop a more realistic expert system, blood sample reports of people affected by DM and Thyroid disorder have been collected from various Endocrine centres in Andhra Pradesh, India. Specificity, Sensitivity, Predictive Values, and Likelihood Ratios of the proposed system are promising in support of system functionality

Keywords

Medical Expert System, Diabetes Mellitus, Thyroid Disorder, Fuzzy Logic, Machine Learning.

How to Cite this Article?

Samsani,S.,&Suma,G.J. (2019). A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients. i-manager’s Journal on Pattern Recognition, 5(4), 44-50. https://doi.org/10.26634/jpr.5.4.15947

References

[1]. Ambilwade, R., Manza, R., & Kaur, R. (2016). Prediction of Diabetes Mellitus and its Complications using Fuzzy Inference System. International Journal of Emerging Technology and Advanced Engineering, 6(7), 2250-2459.

[2]. Carvajal, D. N., & Rowe, P. C. (2010). Sensitivity, specificity, predictive values, and likelihood ratios. Pediatr Rev., 31(12), 511-513.

[3]. Chaker, L., Ligthart, S., Korevaar, T. I., Hofman, A., Franco, O. H., Peeters, R. P., & Dehghan, A. (2016). Thyroid function and risk of type 2 diabetes: A population-based prospective cohort study. BMC Medicine, 14(1), 150.

[4]. Chandgude, N., & Pawar, S. (2016). Diagnosis of diabetes using fuzzy inference system. In 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), (pp.1-6). IEEE.

[5]. Chaturvedi, K., Patel, R., & Swami, D. K. (2015). Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach. International Journal of Advanced Computer Science and Applications, 6(6), 103-110.

[6]. Choi, B. G., Rha, S. W., Kim, S. W., Kang, J. H., Park, J. Y., & Noh, Y. K. (2019). Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks. Yonsei Medical Journal, 60(2), 191-199.

[7]. Duntas, L. H., Orgiazzi, J., & Brabant, G. (2011). The interface between thyroid and diabetes mellitus. Clinical Endocrinology, 75(1), 1-9.

[8]. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32-57.

[9]. Gronich, N., Deftereos, S. N., Lavi, I., Persidis, A. S., Abernethy, D. R., & Rennert, G. (2015). Hypothyroidism is a risk factor for new-onset diabetes: a cohort study. Diabetes Care, 38(9), 1657-1664.

[10]. Hage, M., Zantout, M. S., & Azar, S. T. (2011). Thyroid disorders and diabetes mellitus. Journal of Thyroid Research, 2011, Article ID 439463. http://dx.doi.org/10.4061/2011/439463

[11]. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In ACM Sigmod record, 29(2),1-12.

[12]. Harrar, S. (2019). Low Thyroid Hormone Raises Risk of Type2 Diabetes, Retrieved from https://www.endo crineweb.com/news/diabetes/55372-low-thyroid- hormone- raises-risk-type-2-diabetes

[13]. Jayanthi, N., Babu, B. V., & Rao, N. S. (2017). Survey on clinical prediction models for diabetes prediction. Journal of Big Data, 4(1), 26. DOI: 10.1186/s40537-017- 0082-7

[14]. Johnson, J. L., & Duick, D. S. (2002). Diabetes and thyroid disease: A likely combination. Diabetes Spectrum, 15(3), 140-142.

[15]. Kost, R., Littenberg, B., & Chen, E. S. (2012). Exploring generalized association rule mining for disease co- occurrences. In AMIA Annual Symposium Proceedings, (pp.1284-1293).

[16]. Li, Y., Li, H., & Yao, H. (2018). Analysis and Study of Diabetes Follow-Up Data using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017. Computational and Mathematical Methods in Medicine.

[17]. Norman, A. (2018). Your future doctor may not be human. This is the rise of AI in Medicine. Futurism–SciFi Visions. Çevrimiçi (Erişim, 1 Şubat 2018), Retrieved from https://futurism. com/ai-medicine-doctor.

[18]. Patricia, W. (August, 2017). Thyroid Disordrs and Diabetes. Retrieved from https://www.diabetesself management.com/about-diabetes/general-diabetes- information/ thyroid-disorders-and-diabetes/

[19]. Sneha, N., & Gangil, T. (2019). Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data, 6(1), 13

[20]. Wang, C. (2015). A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making. Theses and Dissertations. Paper 1665.

[21]. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.

[22]. Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., Yang,G., & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International Journal of Medical Informatics, 97, 120-127.

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