IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 173

A Novel Approach for Thyroid Disease Identification Empowered with Fuzzy Logic

Amjad Hussain1, Syed Anwar Hussnain2, Abeer Fatima3, Shahan Yamin Siddiqui2,4, Anwar Saeed5, Yous af Saeed6, Aiesha Ahmed2, Muhammad Adnan Khan7*

1School of Computer Science, National College of Business Administration & Economics, Gujrat Campus, Gujrat, Pakistan 2School of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan. 3Nishter Medical College, Multan, Pakistan. 4Department of Computer Science, Minhaj University, Lahore, Pakistan 5Department of Computer Science, Virtual University, Islamabad, Pakistan 6Department of Computer Science, University of Haripur, KPK, Pakistan 7Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Abstract The human body consists of several glands. The thyroid In the proposed research, A Multi-layered Fuzzy Mamdani gland is termed as the endocrine glands located underneath Inference System (ML-MFIS) is set to analyze the prevailing the voice box [2]. These glands perform different functions Thyroid Disease (TD) which is termed as a common Thyroid by releasing hormones like iodine circulation etc. disorder which leads to different diseases. The Proposed Expert converting it along with amino acid tyrosine to hormone System (TDI-EFL-ES) based on the symptoms and tests, used for thyroxin and triiodothyronine [3]. These hormones help to diagnosis of the thyroid disease. The propose Expert system has been designed for non-specialist people by providing skills like control the growth plus metabolism [4] and also body specialists to get accurate results. The Thyroid Disease functions like the body's ability to change calories into Identification Empowered with Fuzzy Logic Expert System is energy. Thyroid infection is an ailment also known as a based on two layers. Both layers show the input variables. In disorder that affects the thyroid gland. According to the Layer-1, use six input variables that identified the condition of Journal of Pakistan Medical Association (JPMA), 73% Thyroid. Then in Layer-II, more tests are done such as were diagnosed in females and 27% were diagnosed in Stimulating the Thyroid hormone (STH), Triiod-othyronine (T3), males. This disease occurs in women whose ages are 31 to Thyr-oxine (T4), Neck Ultrasound, Thyroid Stimulating Module 40 years and men which are 41 to 50. They performed (TSM) to determine the disease type whether it is treatment on males and females whose ages were ranged Hyperthyroidism or Hypothyroidism. Hyperthyroidism is caused when thyroid releases too many hormones. Hypothyroidism is a from 30 to 60 [5]. common condition characterized by too little thyroid hormone. Hyperthyroidism and Hypothyroidism are types of Thyroid In this research, presents the analysis of the accurate results using Diseases which are based on hormones. Several different proposed Thyroid Disease Identification Empowered with Fuzzy diseases can arise when the thyroid produces too many Logic with the help of medical specialists, collected from Sheikh hormones or not enough [6, 7]. The Thyroid gland is based Zaid Hospital, Lahore, Pakistan. TDI-EFL Expert system has on two glands that produce hormones. achieved 85.33% accuracy in the diagnosis of Thyroid Disease. 1. Follicular cells- That produce and store thyroid Key Words: hormones called triiodothyronine (T3) & thyroxin (T4). TD, MFIS, ML-MFIS, TDI-EFL-ES, hypothyroidism, 2. Parafollicular cells - That produce and secrete a hormone hyperthyroidism that helps regulate the level of calcium in the body [8, 9]. Prognostically, differentiated thyroid disease is seen 1. Introduction: mostly in Follicular cells and papillary thyroid originated as Hyperthyroidism in which the production of the People in our society are suffering from various diseases hormones is very high international and thyroid- like AIDS, CANCER, and HEPATITIS, etc. Unlike other stimulating hormones are very low and Hypothyroidism in diseases, the thyroid can cause disorder which can result in which hormones production is very low. two diseases Hypothyroidism and Hyperthyroidism. Some For the diagnosis of disease, analysis is the key element. are common ailments that can be cured easily, some can be Poor prognosis can result in false treatments. For analysis, difficult to cure but can be successful as well and some we have to examine the signs and symptoms, the period of diseases cannot be cured. The diseases which are cured disease and if it is or the treatment is prolonged and also its may show certain signs of illness occurring again. Certain severity [10, 11]. To see if Thyroid disease is diagnosed or diseases are Chronic. Some diseases can be contagious as not it is important to see if the patient is addicted to well [1]. different liquors like alcohol, cold drinks, etc., drugs,

Manuscript received January 5, 2020 Manuscript revised January 20, 2020

174 IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 smoking. If the patient's any family member was diagnosed Expert methodology system. In which he discussed the in history. The causes can lead to the low or high knowledge-based system, artificial intelligence and also its production of hormones with the signs of neck/throat pain, importance in the medical field, representation of an expert weight loss, appetite, intolerance, constipation, swelling of system in the set of rules formation. The researcher the neck, myxedema and loose stools, etc. [12]. It is investigated the thyroid functioning of too high or too low important to analyses the disease at the right time without production of hormones by the help of Fuzzy rules via the procrastination of the treatment and ignoring the symptoms. neuro-fuzzy method. He kept in view the benefit of There are many tests for the detection of thyroid ailment students' training in the medical field [21, 22]. which usually include the tests which doctors employ to do Thyroid Disease Diagnosis using Neural Networks defined to check the thyroid function like T4, T3 and Thyroid Thyroid Hormones which are produced by Thyroid glands Stimulate Hormones (TSH) test and Thyroid Stimulate and aids in boosting up the metabolism of living being’s Module (TSM) test [13]. Also, imaging tests are into the bodies. According to the researcher, two abnormalities are determination stage and to identify the roots of disease associated with the production of hormones. The Systems related to thyroid like Ultrasound, Thyroid scan, are Hyperthyroidism and Hypothyroidism. He also radioactive iodine uptake test, Computed tomography (CT) discussed the main problem of Thyroid Disease Diagnosis scans Magnetic resonance imaging (MRI), etc. [14]. After through Thyroid data. He used multi-layered, learning of these tests, if all reports or some of these reports show the vector quantization with the help of neural networks and signs of the abnormal production of hormones and are probabilistic with the help of a machine learning database positive then the next steps like surgical procedures and in his research [23- 25]. medications are suggested. According to the American Fuzzy sets are used in the medical field to overcome the Thyroid Association, there's no cure for hypothyroidism unclear, vague, uncertain, wrong measurements diagnosis. and hyperthyroidism. However, some medications can treat In their research titled "An inference system of fuzzy for the disease [15, 16]. The T4 tests are mostly done for Detection of Hypothyroidism," he has discussed only the Hyperthyroidism and T3 tests are done for Hypothyroidism brought disorder of thyroid which is Hypothyroidism. The [17]. But T4 and T3 are performed together and if both tests complicated problems of medical diagnosis which involve show confusing results then Thyroid-stimulating module different factors and solutions involving human abilities tests decide whether it is Hyper or Hypo-Thyroids. are furthered discussed. He has explained the use of Fuzzy The projected Thyroid Expert Disease system is conducted logic in pulmonary embolism, cortical malformations, on these said test conclusions. The numerous analysis hepatitis diagnosis. A process of human thinking can be methods like machine learning, statistical, the abstraction modeled by the inference system of fuzzy. In this proposed of data and information, support of decision system and research, he explained about the uncertain symptoms and expert related system. The use of Expert system techniques correct diagnosis [4, 26-28]. in the medical analysis is increasing for the attaining Fuzzy-based rule of an expert system for the detection of accurate results and decrease the costs by the time [18]. disease relating to thyroid", they have talked about the In this era, Artificial Intelligence is also used in the medical gland diseases in bodies of human but studied on the field as a diagnostic service to examine diseases of different disease of thyroid gland focusing on the function of thyroid types. Various systems are brought to the development glands. The researchers discussed the ENT experts and stage using AI to sort-out and settle the therapeutic issues. how they are few which causes having uncertain data and The inference Systems of fuzzy the also known as fuzzy measurement so they proposed along with the idea of sets [19] is applied in different medical fields for the establishing a fuzzy expert system that truly relies on the solutions of complicated medical diagnoses. And not only fuzzy rules to remove the uncertainty in making the final in medical but ML-FIS is also used in other fields like decision. He used rules of fuzzy through the k-algorithm computer science, arts, etc. The inference system of fuzzy and the algorithm for conjugate scaled gradient for the is a linguistic framework through which the human determination of parameter values [29, 30]. thinking tank and methodology can be molded using the Computational Intelligence approaches like Fuzzy system Inference System of fuzzy-based on Hypothyroidism, a [31, 32], Neural Network [33], Swarm Intelligence [34] & major disease of the thyroid and also worked on Linguistic Evolutionary Computing [35] like Genetic Algorithm [36, Hedges Neural-Fuzzy Classifier with Selected Features 37], DE, Island GA [38], Island DE [39, 40] are strong (LHNFCSF) regarding thyroid diseases [7, 20]. candidate solutions in the field of smart city [41-44] and wireless communication [45-48]. 2. Literature Review:

Thyroid Disease Expert system Diagnose" depicts, the study of Thyroid ailment through the employment of an IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 175

3. Fuzzy System Methodology contains two layers as shown in figure 2. Layer-1 shows the symptoms (Yes/No) by which Thyroid Disease is This section explains our project, Diagnosis of Thyroid diagnosed including six input variables. If the Layer-1 Disease (DTD) using the Multi-Layered Inference System indicates the diagnoses of Thyroid Disease then Layer-2 Mamdani Fuzzy (ML-ISMF) based on the System activates. Layer-2 shows the type of Thyroid Disease based Expertise. Figure 1 illustrates the TDI-EFL Expert System on the input variables of Layer 1. There are five input methodology flow. The proposed TDI-EFL Expert System variables in Layer-2 as shown in figure 2.

Fig. 1 Proposed Thyroid Disease Identification TDI-EFL-ES Methodology

Fig. 2 Proposed Thyroid Disease Identification TDI-EFL-ES Model 176 IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020

LT<0.5 Low Layer-1 of TDI-EFL Expert System can be written 1 TSH B/W 0.5 – 3 Normal mathematically as: GT>3 High LT<4.6 Low µDI,Layer−1 = MFIS B/W 4.6 – Normal [µ ,µ , µ , µ , 2 T4 11.2 푊푒𝑖푔푡ℎ 퐴푝푝푒푡𝑖푡푒 퐼푛푡표푙푒푟푎푛푐푒 퐶표푛푠푡𝑖푝푎푡𝑖표푛 GT>4.6 High LT<100 Low µ푀푦푒푥푒푑푒푚푎, µ퐿표표푠푒 푠푡표표푙 ] 3 T3 B/W 100-200 Normal GT>200 High LT <100 Low

4 N-U B/W 100 -200 Normal And Layer-2 of TDI-EFL Expert System can be shown as: GT>200 High µ =MIFS LT<1 No 푇퐷퐼,퐿푎푦푒푟2 5 TSM GT>1 Yes [µ퐷퐼,퐿푎푦푒푟1, µ푇푆퐻, µ푇4, µ푇3, µ푁−푈, µ푇푆푀] 3.2 Output Variables: 3.1 Input Variables In this research, a multi-layered fuzzy inference system In the Fuzzy system, input variables are numerical values based on the expert system shows the diagnosis of thyroid that are used to detect Thyroid Disease. Adding both layers, disease. Layer-II activates only if Layer-I is yes. Output there is a total of eleven input values. Six input values are variables of both layers are as follow: in Layer-I and five are in Layer-II. Table 1 and 2 elaborates these input values regarding their ranges which are as Table 3: Output variables for Proposed TDI-EFL Expert System Output follow: Sr # Layers Variables Semantic sign 1 Layer-I DI Negative Table 1: Input variables of Layer-I for Proposed TDI-EFL Expert Positive System No Thyroid Input Semantic 2 Layer-II TDI Infection Sr# parameters Ranges signs Hypo Thyroid LT<10 Loss Hyper Thyroid 1 Weight GT>10 Gain LT<10 Low 3.3 Membership Functions: 2 Appetite GT>10 High LT<0.5 Cold 3 Intolerance GT>0.5 Heat In this system, graphical values are shown between 0, 1 and LT<0.3 No also present the arithmetical functionality which proposes 4 Constipation GT>0.3 Yes LT<0.2 No statistical values of input and output variables. Graphical 5 Myxedema GT>0.2 Yes and arithmetical demonstration of TDI-EFL Expert System LT<2 No membership functions of input/output variables of both 6 Loose Stool GT>2 Yes layers is shown in Table 4, Table 5 and Table 6. These Table 2: Input variables of Layer-II for Proposed TDI-EFL Expert membership functions are presented after the discussion System with medical experts from Sheikh Zaid Hospital, Lahore, Input Semantic Pakistan. Sr# parameters Ranges signs

Table 4: Input Variables Membership Functions of Layer-I for Proposed TDI-EFL Expert System Sr. No . Input variables Membership Functions Graphical representation

µ퐸,퐿(푒) 1 10 − e = {푚푎푥 (푚푖푛 (1, ) , 0)} Weight=E 5 µ퐸,퐺(푒) = ((휇퐸(e)) e − 5 {푚푎푥 (푚푖푛 ( , 1) , 0)} 5

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2 µ퐴,퐿(a) 10 − a = {푚푎푥 (푚푖푛 (1, ) , 0)} Appetite=A 1

((µ퐴(a)) µ퐴,퐻(a) ={푚푎푥 (푚푖푛 ( a−9 , 1) , 0)} 1

3 µI,C(i) Intolerance= 0.5 − i = {푚푎푥 (푚푖푛 (1, ) , 0)} I 0.1 µI,H(i) ((µ (푖)) i−0.4 퐼 ={푚푎푥 (푚푖푛 ( , 1) , 0)} 0.1

µC,N(c) 4 Constipation 0.3 − c = {푚푎푥 (푚푖푛 (1, ) , 0)} = 0.05 C µC,Y(c)

((µ (푐)) c−0.25 퐶 ={푚푎푥 (푚푖푛 ( , 1) , 0)} 0.05

Myexedema= µM,N(m) 5 M 0.2 − m = {푚푎푥 (푚푖푛 (1, ) , 0)} 0.1 ((µ푀(푚)) µM,Y(m) ={푚푎푥 (푚푖푛 ( m−0.1 , 1) , 0)} 0.1

2 − l µ (l) = {푚푎푥 (푚푖푛 (1, ) , 0)} 6 Loose Stool= L L,N 1 µL,N(l) l−1 ((µ퐿(푙)) ={푚푎푥 (푚푖푛 ( , 1) , 0)} 1

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Table 5: Input Variables Membership Functions of Layer-II for Proposed TDI-EFL Expert System Sr Input no variables Membership Functions Graphical representation 0.5 − x µ (x) = {푚푎푥 (푚푖푛 (1, ) , 0)} X,L 0.1 1 TSH= µX,N(x) X 푥 − 0.4 3 − 푥 = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} 0.1 0.5 ((µ푋(푥)) x−2.5 µ (x) ={푚푎푥 (푚푖푛 ( , 1) , 0)} X,H 0.5

2 4.6 − z µ (z) = {푚푎푥 (푚푖푛 (1, ) , 0)} Z,L 0.1 T4= Z µZ,N(z) ((µ (푧)) 푧 − 4.6 11.2 − z 푍 = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} 0.1 0.1

µ (z) ={푚푎푥 (푚푖푛 ( z−11.1 , 1) , 0)} Z,H 0.1 100 − t µ (t) = {푚푎푥 (푚푖푛 (1, ) , 0)} 3 T,L 5

T3=T µT,L(t) 푡 − 95 200 − t = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} ((µ푇(푡)) 5 5

µ (t)={푚푎푥 (푚푖푛 ( t−195 , 1) , 0)} T,L 5

0.5 − v µ (v) = {푚푎푥 (푚푖푛 (1, ) , 0)} V,L 0.1 4 N-U=V µV,L(v) ((µ (푣)) 푣 − 0.4 1.5 − v 푉 = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} 0.1 0.1

µ (v)={푚푎푥 (푚푖푛 ( V−1.4 , 1) , 0)} V,L 0.1 IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 179

5 1 − y µ (y) = {푚푎푥 (푚푖푛 (1, ) , 0)} TSM=Y Y,L 0.1

((µ (푦)) y−0.9 푌 µY,L(y)={푚푎푥 (푚푖푛 ( , 1) , 0)} 0.1

Table 6: Output Variables Membership Functions for Proposed TDI-EFL Expert System Sr # Layers Output Variables Membership functions Graphical Representation ((µ (푑푖)) 퐷퐼,푁푒푔푎푡𝑖푣푒 0.5 − di Diagnosis = {푚푎푥 (푚푖푛 (1, ) , 0)} 0.1 1 Layer-I Infection ((µ퐷퐼,푃표푠𝑖푡𝑖푣푒 (푑푖)) ((µ퐷퐼(푑푖)) di − 0.4 = {푚푎푥 (푚푖푛 ( , 1) , 0)} 0.1

((µ푇퐷퐼,푁표푇ℎ푦푟표𝑖푑(휙))

0.3 − ϕ = {푚푎푥 (푚푖푛 (1, ) , 0)} 0.05

((µ푇퐷퐼,퐼푛푓푒푐푡𝑖표푛(휙)) Thyroid Disease 휙 − 0.25 0.6 − ϕ = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} Identification 0.05 0.05 2 Layer-II ((µ (휙)) ((µ (휙)) 푇퐷퐼 푇퐷퐼,HypoThyroid푒 휙 − 0.55 0.75 − α = {푚푎푥 (푚푖푛 ( , 1, ) , 0)} 0.05 0.05

((µ푇퐷퐼,HyperThyroid (휙))

={푚푎푥 (푚푖푛 ( ϕ−0.7 , 1) , 0)} 0.05

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3.4 Lookup Table Norm Norm Norm Infectio 9 al al al No Yes n Norm The developed lookup table of Layer-II of the Proposed 10 al High High No No Hypo Expert System TDI-EFL covers input-output rules. There Norm Infecte 11 al High High d Yes Hyper is a total of 162 rules. Only a few of them are shown below 12 High Low High Infecte Yes Hyper in table 7. Table 7 is also established with the discussion of d Norm Norm medical expertise of Sheikh Zaid Hospital, Lahore, 13 High al al No No No Pakistan 14 Low Low Low Low Yes hyper 15 Low Low Low Low No Hypo Table 7: Rules-Based Lookup Table for Proposed TDI-EFL Rule TS 3.4.1 Rule-Based s TSH T4 T3 N-U M Results Norm Norm 1 Low al al Yes No Hypo I/O rules play an essential role in any Fuzzy Inference System (FIS). The presentation of an expert system 2 Low Norm Norm Yes Yes hyper al al depends upon these rules. In this project, I/O is made using Norm Norm Infecte Infectio 3 Low al al d No n a lookup table as shown in table 7. I/O rules of layer-I & Norm Norm Infecte layer-II based on the TDI-EFL Expert System is shown in 4 Low al al d Yes Hyper Norm figure 3 and figure 4. 5 Low al High No No Hypo Norm Norm Infecte 3.4.2 Inference Engine 6 al al Low d Yes Hyper Norm Norm Norm 7 al al al No No No Inference Engine is concluded to be one of the mentioned Norm Norm Norm Infectio crucial elements of an expert system. The analysis shows 8 al al al No Yes n that Mamdani Inference Engine is utilized in both layers.

Fig. 3 Rules-based on layer-I for Proposed TDI-EFL

Fig. 4 Rules-based on layer-II for Proposed TDI-EFL IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 181

3.4.3 De-fuzzifier

It is an essential component of the Mamdani Fuzzy- Inference Expert System. There are various types of De- fuzzifier. The centroid type of de-fuzzifier is used in this project. All the figures below represent the graphical view of layers at De-fuzzifier. Figure 5 (rules surface for weight and myxedema) illustrates the use of graphical representation of layer-1 in the TDI-EFL Proposed Mamdani Fuzzy-Inference Expert System. And graphical figures 5a, 5b, 5c, and 5d demonstrates the graphical representation of layer-2 in TDI-EFL Expert System. Fig. 5 layer-1, Rules surface for Weight and Myxedema

Fig. 5a Layer-2 for TSM and T4 Fig. 5b Layer-2 for TSH and N-U

Fig. 5c Layer-2 for TSM and N-U Fig. 5d Layer-2 for TSM and TSH

The Detection of Thyroid ailment is evaluated by using the if it is higher or lower, there is a disorder which can lead to possibility based on input variables of layer-1. If the Hyperthyroidism or hypothyroidism but if there is normal parameter's values are elevated up to a certain statistical production of hormones plus results of TSH and TSM are value, 85% of Thyroid disease can be seen in this case. The also normal then there can be no Thyroid disorder. In figure patient can show various severe symptoms if the 5b Graphical view of TSH and N-U is shown. TSH is production of the hormones is more than normal range or normal if it ranges between 0.5 – 3 mIU/L. N-U can differ less than normal then. The chances of diagnosing Thyroid in different cases. If the TSH>T4, or TSH

Fig. 6a Result of Proposed TDI-EFL Expert System for Negative Infection

Fig. 6b Result of Proposed TDI-EFL Expert System for Infection with Hypo Thyroid IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 183

Fig. 6c Result of Proposed TDI-EFL Expert System for Infection with Hyper Thyroid

Figure 6a illustrates, If ((휇퐸(e)) is considered a loss, References ((µ퐴(a)) is low, ((µ퐼(푖)) is cold, ((µ퐶(푐)) 푖푠 no, ((µ푀(푚)) [1] Hessel, A., Chalian, A. A., & Clayman, G. L. (2008). is no, ((µ퐿(푙))is no the output for ((µ퐷퐼(푑푖)) is negative. Surgical management of recurrent thyroid cancer. Figure 6b illustrates, If (( µ푋(푥)) is considered low, Neuroimaging Clinics of North America, 18(3), 517-525. [2] Anjara, F., & Jaharadak, A. A. (2019, February). Expert ((µ푍(푧)) is considered low, ((µ푇(푡)) is considered low, ((µ (푣)) is considered low, ((µ (푦)) is no, then the output System for Diseases Diagnosis in Living Things: A 푉 푌 Narrative Review. In Journal of Physics: Conference Series for ((µ푇퐷퐼(휙)) is hypo Thyroid. (Vol. 1167, No. 1, p. 012070). IOP Publishing. Figure 6c illustrates, If (( µ푋(푥)) is considered low, [3] Rojeski, M. T., & Gharib, H. (1985). Nodular thyroid ((µ푍(푧)) is considered low, ((µ푇(푡)) is considered low, disease: evaluation and management. New England Journal ((µ푉(푣)) is considered normal, ((µ푌(푦)) is considered yes, of Medicine, 313(7), 428-436. then the output for ((µ푇퐷퐼(휙)) is hyper Thyroid. [4] Khanale, P. B., & Ambilwade, R. P. (2011). A fuzzy inference system for diagnosis of hypothyroidism. Journal of Artificial Intelligence, 4(1), 45-54. 5. Conclusions and Future Work [5] Khosravi, M., Yazdanshenas, M., & Nemati, M. H. (2015). Design of an expert system for the diagnosis of thyroid The consolidated purpose of the proposed project is to cancer. Cumhuriyet Ü niversitesi Fen-Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 1420-1424. design an entire Expert system to diagnose thyroid disease. [6] Rezaei, S., & Arab, M. (2016). Effects of the new health The proposed TDI-EFL Expert System is providing ease reform plan on the performance indicators of Hamedan for the usage in medicine especially for both Medical university hospitals. Journal of School of Public Health and professionals and non-professionals. Properly designed Institute of Public Health Research, 14(2), 51-60. and detection on time along with accurate and specified [7] Azar, A. T., Hassanien, A. E., & Kim, T. H. (2012). Expert treatment can cover a distanced methodology towards system based on neural-fuzzy rules for thyroid disease decreasing the causalities ratio associated with this state of diagnosis. In Computer Applications for Bio-technology, ailment. The TSH and T4 concentrations of blood turn out Multimedia, and Ubiquitous City (pp. 94-105). Springer, to be the standard methodology for the detection of disease. Berlin, Heidelberg. [8] Mense, M. G., & Boorman, G. A. (2018). Thyroid Gland. In Overall, the recent 5-years report for the survival rate of Boorman's Pathology of the Rat (pp. 669-686). Academic people with thyroid cancer is 98%. Whereas the ratios of Press. survival rely on innumerable factors and reasons include [9] Smith, M. S. (2009). H. Maurice Goodman: Basic Medical the types of thyroid cancer specifically and the disease Endocrinology. stage. Approximately 2% of thyroid cancer are found in [10] DO PANTALONE, K. M., & Nasr, C. (2010). Approach to kids and teenagers. The ratio and rate of accuracy of the a low TSH level: patience is a virtue. Cleveland Clinic Proposed TDI-EFL Expert system is 85.33% conducted journal of medicine, 77(11), 803. form Sheikh Zaid Hospital, Lahore, Pakistan with the help [11] Sajadi, M., Niazi, N., Khosravi, S., Yaghobi, A., Rezaei, M., of medical experts. & Koenig, H. G. (2018). Effect of spiritual counseling on spiritual well-being in Iranian women with cancer: A

randomized clinical trial. Complementary therapies in clinical practice, 30, 79-84. [12] Baheti, M. (2016). Study of need and framework of expert systems for medical diagnosis. IOSR J Comput Eng. (OSR- JCE) e-ISSN, 2278-0661. 184 IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020

[13] Zadeh, L. A. (1965). Fuzzy sets, information, and control. Diagnosis of Arthritis Using Adaptive Hierarchical vol, 8, 338-353. Mamdani Fuzzy Type-1 Expert System. [14] Hatzilygeroudis, I., Vassilakos, P. J., & Tsakalidis, A. (1997). [31] Hussain, S., Abbas, S., Sohail, T., Adnan Khan, M., & Athar, XBONE: A hybrid expert system supporting the diagnosis of A. Estimating virtual trust of cognitive agents using multi- bone diseases Networks (ANNs), 5(6), 2. layered socio-fuzzy inference system. Journal of Intelligent [15] Watson, T. (2008). CauseWired: plugging in, getting & Fuzzy Systems, (Preprint), 1-16. involved, and changing the world. John Wiley & Sons. [32] Areej Fatima, Muhammad Adnan Khan, Sagheer Abbas, [16] GRANGER, J. Annual crime report shows a rise in assaults. Muhammad Waqas, Leena Anum, and Muhammad Asif, [17] Massomi, Z., Khani, S., Gharosian, M., Farhadian, M., & Evaluation of Planet Factors of Smart City through Multi- Shayan, A. (2016). The prevalence of abnormal Pap smears layer Fuzzy Logic (MFL). The ISC Int'l Journal of in females referred to health centers affiliated to medical Information Security. 51-58, 2019. sciences during the Years 2012 to 2016. J Educ Community [33] Atta, A., Abbas, S., Khan, M. A., Ahmed, G., & Farooq, U. Health, 3(2), 16-22. (2018). An adaptive approach: Smart traffic congestion [18] Afshari, D., Rafizadeh, S., & Rezaei, M. (2012). Sciences, control system. Journal of King Saud University-Computer Kermanshah, Iran b Department of Biostatistics, and Information Sciences. Kermanshah University of Medical Sciences, Kermanshah, [34] Khan, M. A., Umair, M., Saleem, M. A., Ali, M. N., & Abbas, Iran. International Journal of Neuroscience, 122(2), 60-68. S. (2019). CDE using improved opposite-based swarm [19] Hennessey, J. V., & Scherger, J. E. (2007). Evaluating and optimization for MIMO systems. Journal of Intelligent & treating the patient with hypothyroid disease. The Journal of Fuzzy Systems, 37(1), 687-692. family practice, 56(8 Suppl Hot Topics), S31-9. [35] Khan, M. A., Umair, M., & Choudhry, M. A. S. (2015). GA [20] Zhang, G. P., & Berardi, V. L. (1998). An investigation of based adaptive receiver for MC-CDMA system. Turkish neural networks in thyroid function diagnosis. Health Care Journal of Electrical Engineering & Computer Sciences, Management Science, 1(1), 29-37. 23(Sup. 1), 2267-2277. [21] Stankov, S., Glavinic, V., & Rosie, M. (2011). Design, [36] Khan, M. A., Umair, M., & Choudry, M. A. S. (2015, Implementation and Evaluation. December). Island differential evolution based adaptive [22] Dogantekin, E., Dogantekin, A., & Avci, D. (2011). An receiver for MC-CDMA system. In 2015 International expert system based on Generalized Discriminant Analysis Conference on Information and Communication and Wavelet Support Vector Machine for diagnosis of Technologies (ICICT) (pp. 1-6). IEEE. thyroid diseases. Expert Systems with Applications, 38(1), [37] Ali, M. N., Khan, M. A., Adeel, M., & Amir, M. (2016). 146-150. Genetic Algorithm based adaptive Receiver for MC-CDMA [23] Oztekin, H., Temurtas, F., & Gulbag, A. (2014). BZK. SAU. system with variation in Mutation Operator. International FPGA10. 1: A modular approach to FPGA‐based Journal of Computer Science and Information Security, microcomputer architecture design for educational purposes. 14(9), 296. Computer Applications in Engineering Education, 22(2), [38] Umair, M., Khan, M. A., & Choudry, M. A. S. (2015, 272-282. December). Island genetic algorithm-based MUD for MC- [24] Er, O., & Temurtas, F. Human Face Recognition. Electronic CDMA system. In 2015 International Conference on Letters on Science&Engineering, 2(1), 1-12. Information and Communication Technologies (ICICT) (pp. [25] Temurtas, H., Yumusak, N., & Temurtas, F. (2009). A 1-6). IEEE. comparative study on diabetes disease diagnosis using neural [39] Umair, M., Khan, M. A., & Choudry, M. A. S. (2013, networks. Expert Systems with Applications, 36(4), 8610- January). GA backing to STBC based MC-CDMA systems. 8615. In 2013 4th International Conference on Intelligent Systems, [26] Mishra, N., & Jha, P. (2014). A review of the applications of Modelling, and Simulation (pp. 503-506). IEEE. the fuzzy expert system for disease diagnosis. International [40] Kashif, I., Muhammad, A.K., Sagheer, A., Zahid, H., & Journal of Advanced Research in Engineering and Applied Areej, F (2018). Intelligent Transportation System (ITS) for Sciences, 3 (12), 28-43. Smart-cities using Mamdani Fuzzy Inference System, [27] Shroff, S., Pise, S., Chalekar, P., & Panicker, S. S. (2015, International Journal of Advanced Computer Science and January). Thyroid disease diagnosis: A survey. In 2015 IEEE Applications (IJACSA). ISSN: 2158-107X, Vol. 9, No. 2, 9th International Conference on Intelligent Systems and (pp. 94-105), Digital Object Identifier (DOI): Control (ISCO) (pp. 1-6). IEEE. 10.14569/IJACSA.2018.090215. [28] Smallridge, R. C., Ain, K. B., Asa, S. L., Bible, K. C., [41] Fatima, Areej, Sagheer Abbas, Muhammad Asif, and Brierley, J. D., Burman, K. D., & Shah, M. H. (2012). Muhammad Khan. "Optimization of Governance Factors for American Thyroid Association guidelines for the Smart City Through Hierarchical Mamdani Type-1 Fuzzy management of patients with anaplastic thyroid cancer. Expert System Empowered with Intelligent Data Ingestion Thyroid, 22(11), 1104-1139. Techniques." EAI Endorsed Transactions on Scalable [29] Biyouki, S. A., Turksen, I. B., & Zarandi, M. F. (2015, Information Systems 6, no. 23 (2019). August). Fuzzy rule-based expert system for diagnosis of [42] Abbas, Sagheer, Tahir Alyas, Atifa Athar, Muhammad Khan, thyroid disease. In 2015 IEEE Conference on Computational Areej Fatima, and Waseem Khan. "Cloud Services Ranking Intelligence in Bioinformatics and Computational Biology by measuring Multiple Parameters using AFIS." EAI (CIBCB) (pp. 1-7). IEEE. Endorsed Transactions on Scalable Information Systems 6, [30] Siddiqui, S. Y., Hussnain, S. A., Siddiqui, A. H., Ghufran, no. 22 (2019). R., Khan, M. S., Irshad, M. S., & Khan, A. H. (2019). IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020 185

[43] Tabassum, N., Khan, M., Abbas, S., Alyas, T. and Athar, A., Processing and Cognitive machines with various publications in 2019. Intelligent reliability management in hyper- international journals and conferences convergence cloud infrastructure using fuzzy inference system. EAI Endorsed Transactions on Scalable Information Dr. Anwar Saeed has joined Virtual Systems, 6(23). University (VU) of Pakistan in April 2006 [44] Naz, N.S., Abbas, S., Khan, M.A., Abid, B., Tariq, N. and and is currently working as an Assistant Farrukh, M., Efficient Load Balancing in Cloud Computing Professor in the Computer Science using Multi-Layered Mamdani Fuzzy Inference Expert Department. He has obtained his Ph.D. System. Degree in computer science from the [45] MATLAB for Artificial Intelligence (www.mathworks.com). National College of Business [46] Khan, M.A., Umair, M. and Choudhry, M.A.S., 2013. Administration & Economics (NCBA&E), Acceleration to LMS based STBC MC-CDMA receiver. Int. Lahore, Pakistan. His area of research is a J. Scientific & Engineering Research, 4(8), pp.925-929. key generation for data encryption and [47] Iqbal, K., Khan, M.A., Abbas, S. and Hasan, Z., 2019. Time information security. He is also interested in Quantum Computing complexity analysis of GA-based variants uplink MC- especially encryption mechanisms used in this field. He is the CDMA system. SN Applied Sciences, 1(9), p.953. author of a monograph on framework for Self Organizing [48] Asif, M., Khan, M.A., Abbas, S. and Saleem, M., 2019, Encryption in Ubiquitous Environment, published by VDM January. Analysis of Space & Time Complexity with PSO Verlag in 2010 Based Synchronous MC-CDMA System. In 2019 2nd International Conference on Computing, Mathematics and Dr. Yousaf Saeed is currently working as Engineering Technologies (iCoMET) (pp. 1-5). IEEE. Assistant Professor at the Department of Information Technology, University of Haripur, Pakistan. He completed his Ph.D. degree in Cognitive VANETs from Mr. Amjad Hussain is currently working as NCBA&E, Lahore, Pakistan, and M.S. a Lecturer at the Department of Computer degree in Broadband and HighSpeed Science, NCBA&E, Gujrat Campus, Communication Networks from the Pakistan. He is doing a Ph.D. from the University of Westminster, London, U.K., School of Computer Science, NCBA&E, where he achieved distinction in Research Thesis on IPv6. He Lahore, Pakistan. Amjad’s research received the International Students Award at the College of North interests primarily include Cloud West London, U.K. He acquired seven Research Projects from the Computing, IoT, Intelligent Agents, Image National Grassroots ICT Research Initiative (NGIRI) Program Processing and Cognitive machines with from the Ministry of Information Technology & Telecom, various publications in international journals and conferences. Government of Pakistan. His achievements include the eighteen publications including three monographs, ten journal articles, and five conference papers. His patent is under review regarding Dr. Syed Anwar Hussnain is currently emergency vehicles-based traffic lights control system. working as an Associate Professor at the Department of Statistics, NCBA&E, Lahore, Pakistan. He completed his Ph.D. Dr. Aiesha Ahmad is working as an in Statistics from School of Statistics, Assistant Professor in the Department of NCBA&E, Lahore, Pakistan. Computer Science, NCBA&E Multan Pakistan. Her area of research is A.I, Machine Consciousness, Deliberative and Non-deliberative Rationality Evaluation, Fuzzy Modeling, Knowledge Base systems.

Ms. Abeer Fatima is doing M.B.B.S from Nishter Medical College, Multan, Pakistan. Her area of research is medical diagnosis. Dr. Muhammad Adnan Khan is currently

working as an Assistant Professor at the Mr. Shahan Yamin Siddiqui is currently working as a Lecturer at the Department of Department of Computer Science, Lahore Computer Science, Minhaj University, Garrison University, Lahore, Pakistan. He Lahore, Pakistan. He is doing a Ph.D. from completed his Ph.D. from ISRA University, the School of Computer Science, NCBA&E, Pakistan by obtaining a scholarship award Lahore, Pakistan. he completed his M.Phil. from the Higher Education Commission, from the UMT, Lahore, Pakistan. Shahan’s Islamabad, Pakistan. He also completed his research interests primarily include Cloud M.Phil. & BS degrees from the International Islamic University, Computing, IoT, Intelligent Agents, Image Islamabad, Pakistan by obtaining scholarship award from the 186 IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.1, January 2020

Punjab Information & Technology Board, Govt of Punjab, Pakistan. Prior to joining the Lahore Garrison University, Khan has worked in various academic and industrial roles in Pakistan. He has been teaching graduate and undergraduate students in computer science and engineering for the past 11 years. Presently, he is guiding 04 Ph.D. scholars and 04 M.Phil. Scholars. He has published about 130 research articles in International Journals as well as reputed International Conferences. Khan's research interests primarily include MUD, Image Processing & Medical Diagnosis, Channel Estimation in Multi-Carrier Communication Systems Using Soft computing with various publications in journals and conferences of international repute.