Table of Contents Author Guidelines Submit a Manuscript
Advances in Fuzzy Systems
Volume 2017 (2017), Article ID 7371634, 8 pages
Research Article

Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets

National School of Engineers (ENIS), REsearch Groups on Intelligent Machines (REGIM), BP 1173, 3038 Sfax, Tunisia

Correspondence should be addressed to Hanen Jemal

Received 8 January 2017; Accepted 28 March 2017; Published 21 May 2017

Academic Editor: Mehmet Onder Efe

Copyright © 2017 Hanen Jemal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS) should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU). Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS), the shown work has its origins in the Modified Early Warning Score (MEWS) standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.

1. Introduction

Medical decision-making in Intensive Care Units (ICU) can be considered as a process, combining both logical cognition and perception. It undertakes analyzing, within complex models of multiple features, usually marked with vague, inaccurate, and inexact information. In a bid to provide an effective model of the ICU process, we design an expert decision support system for the ICU detection of deteriorating patients. This newly novel model is predominantly based on the intuitionistic fuzzy sets (IFS) and the Modified Early Warning Score (MEWS) standard [1].

The major reason for choosing the IFS as a major tool for the development of a decision-making system lies in the fact that IFS are improved better than effectively regarding the situations in which no overlap between the fuzzy sets is perceived. Indeed, imitating the expert’s cognitive decision-making completeness the IFS attempt to achieve the convenient diagnosis, proceeded through maintaining the expert’s detained knowledge as applied and safeguarded within an intelligent system.

Concerning the present paper, the Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS) has actually been implemented and tested within a real life context of Polyclinic ESSALEMA, Sfax, Tunisia. In this context, we focus on a detailed depiction of MIFEDSS based on the MEWS for the purpose of estimating the patient’s risk degree.

This work is organized according to the following layout: following the introduction, Section 2 is devoted highlighting the IFS relevance and prevalence in the healthcare area. Section 3 presents theoretical background about IFS. Then, we reveal the MIFEDSS designed to ICU healthcare system. In Section 5, we present the implementation details of the prototype. Afterwards, we set forth a results and discussion section and then we wind up by a conclusion with further suggestions for additional advancement.

2. IFS in Healthcare Domain

In the literature there are several studies in fuzzy logic (FL) for medical care which were grouped into six area: (i) neuromedical filed [2], (ii) blood glucose monitoring [36], (iii) neck and head cancer [7], (iv) breast cancer classification [2, 8], (v) brain tumor extraction and classification [9], and (vi) emergency decision system [10].

Since Atanassov instigated the concept of IFS [11], this technique has been utilized in different medical case research. The majority of these studies concern the use and the comparison of different IFS measures of similarity such as the distance between interval-valued IFS, the max–min–max composition rule, and Mold Cosine similarity measure. In fact, in [12] researchers use the max–min–max composition rule to detect the illness. This composition neglects extreme values. Similarly, Szmidt and Kacprzyk advance a new approach for medical care diagnosis process through application of the IFS based solution for useful for analyzing the optimally closest symptoms [1315]. In these works, illness is depicted through several syndromes without aggregation of symptoms being involved. For this reason, to solve this issue, other studies present a new measure called interval-valued IFS based on the aggregation [1618]. For instance, Chetia and Das apply an approach for medical care diagnosis based on interval-valued fuzzy soft sets and demonstrate the technique with a theoretical case study [18].

In [17], author proposes a new method for medical care diagnosis: the distance between interval-valued IFS. This approach makes a diagnosis by aggregating several symptoms, using the distance of interval data in order to decrease the loss of data. In the same way, in 2014, authors [16] introduce a new method to sickness diagnosis, using interval-valued intuitionistic fuzzy set with logical operators.

In proposed works in [19], the study of Sanchez’s [20] method of medical diagnosis approach is provided using the IFS theory notion. The idea of intuitionistic medical diagnosis methodology relies heavily on intuitionistic fuzzy relations. For a deeper investigation of the method the authors have provided hypothetical case study described by means of an illustrative flowchart.

In study conducted by Çuvalcıoğlu and Mercan [21] in 2004, the authors have made appeal to a clear definition of weak intuitionistic fuzzy sets along with several diagnosis algorithms through application of a tough method.

In a paper elaborated by [22] various classification methods have been applied to 17 different features, namely, stepwise logistic regression, stepwise discriminant analysis, and nonpulmonary weaning index as well as intuitionistic fuzzy Voronoi diagrams. The proposed algorithm has been applied to solve the classification problem fit for weaning initiation from long-term mechanical ventilation.

In 2011, Hung proposed an entropy measure based on intuitionistic fuzzy sets. An instructive scenario related to medical pattern recognition has revealed the convenience of such a study. Still, in a bid to make easier ranking results, a system interface has been developed to support doctors in constricting and reaching the most efficient decisions [23].

In 2011, Ye set up a cosine similarity measure along with a weighted cosine similarity among IFS. For the purpose of highlighting the proposed measure’s efficiency, the existing similarity measures amid IFS have been assessed by means of cosine similarity measure initiated between IFS through numerical examples applied to pattern recognition and medical diagnosis [24].

Similarly, another Mold Cosine similarity based measure projection formula to medical pattern recognition and intuitionistic fuzzy decision-making has been proposed in 2015 which involves a four pattern mode pertaining to Atanassov’s intuitionistic fuzzy values [25].

In 2013, the authors in [26] survey other distance relating measures (Hamming distance, Euclidean distance, Normalized Hamming distance, and Normalized Euclidean distance) as pattern detection tools for IFS.

Overall, most tools of these researches display some of IFS associated similarity measures with exposing measures application technique with a hypothetic medical case study. In practical medical cases, at date only Khatibi and Montazer [27] and Chaira [2830] seem to put forward a real solution. In [27], for instance, the authors introduce a useful for solving the bacteria classification problem through application of IFS to examine their abilities in coping work of the medical pattern detection related ambiguity.

In 2010 Chaira [28] proposed a new IFS applying framework useful for segmenting poor contrasted blood vessels as well as blood cells in pathological images. Thus, an intuitionistic fuzzy image turns out to be constructed by means of intuitionistic fuzzy Sugeno generator which has been used to retrieve the optimum threshold values. Additionally, Chaira presents an IFS approach relevant to color region extraction [29], in another context providing a novel approach to intuitionistic fuzzy C means clustering method using IFS theory. For the sake of testing and ensuring its efficiency, the devised algorithm has been tested on different regions of the CT scan brain images likely useful for application for brain abnormalities identification purposes [30].

Thus, it can be inferred from the above cited literature that the IFL are successfully applied in several medical applications, as helpful tools useful for monitoring and detecting wide range of disease. So the present work can be considered as the pioneering study whereby a hybrid approach simultaneously combining multiagent system (MAS) and IFS [3133] is deployed in a real application context and environment. Indeed, our system provides a model that combines both the benefits of MAS and IFS. The architecture is composed of a set of autonomous agents adapted to the interaction: the expert agents with IFS based software learning in order to assist agent doctor. In this paper, we propose the modeling, the realization, and the evaluation of the intuitionistic fuzzy inference technique in medical decision (expert agents learning processes in the MAS). Here, we put forward the MIFEDSS based MEWS (Table 5) in ICU deployed in Polyclinic ESSALEMA, Sfax. In the next section we define the theoretical background of the IFS theory.

3. Preliminaries

The intuitionistic fuzzy sets (IFS) were introduced by Atanassov [34] as a generalization of fuzzy sets of Zadeh [35] “which look more accurate to uncertainty quantification and provide the opportunity to precisely model the problem based on the existing knowledge and observations,” where, besides the degree of membership of each one element to a set , the degree of nonmembership was also measured.

Let is a nonempty fixed set. An intuitionistic fuzzy set (IFS) is an object of the form where and are degrees of membership and nonmembership (“falsity degree” or “degree of nonvalidity” [11]) of each , respectively, and for each . A class of all the IFS in is denoted as . The pair , is called “Intuitionistic Fuzzy Pair” [11].

In addition to membership and nonmembership functions, a function of hesitancy or uncertainty of to denoted by must be taken into consideration. is computed as

In real world tasks, we recurrently deal with vague information. Accessible information is occasionally vague, inexact, or inadequate. “There are situations where, due to insufficiency in the information available, the evaluation of membership values is not possible up to our satisfaction. Due to the some reason, evaluation of nonmembership values is not also always possible and consequently there remains a part in determinism on which hesitation survives. Certainly fuzzy sets theory is not appropriate to deal with such problem; rather IFS theory is more suitable” [36].

Indeed, we assume that IFS have been found to be practical to deal with ambiguity.

4. The Proposed MIFEDSS

In this study, we propose the modeling, implementation, and validation of the MIFEDSS; in the previous work [31, 32, 37] we present a multiagent system (MAS) for healthcare interaction in Mobile Cloud Computing Applications; in the MAS system they are different expert agent with intuitionistic fuzzy sets based software learning in order to assist doctor agent and assess patients.

The expert agents and agent doctor role are described in Figure 1.

Figure 1: Agent (doctor and expert) tasks.

In this study, the IFS is founded on the Modified Early Warning Score (MEWS), defined as a clear guide for caregivers in the emergency unit to find the level of sickness of a patient [1]. This MEWS was evaluated in 206 surgical patients over 9 months in 1999. The purpose of the MEWS is to facilitate communication between nursing [38].

The Modified Early Warning Score (MEWS) is a physiological score for estimation and is based on five physiological parameters. The observations included in this scoring are exposed in Table 1: respiratory rate, systolic blood pressure, heart rate, temperature, and Glasgow score or AVPU score.

Table 1: MEWS standard.

A limitation of current MEWS is that they are not capable of modeling the hesitancy introduced into a complex system due to inadequate facts, loss of information, and uncertainty. To handle this issue, we propose a novel extension of the MEWS model which is based on the theory of intuitionistic fuzzy sets (Table 2).

Table 2: Intuitionistic fuzzy MEWS system.

The vital signs monitored that help the medical diagnosis are systolic blood pressure (SBP), heart rate (HR), blood glucose (BG), patient temperature (TEMP), oxygen saturation (O2S), and Kalmy (KAM). A human expert knowledge, from Polyclinic ESSALEMA, was used for the determination of the input fields. Three experts in ICU department were asked to be involved in this study. For that, in order to calculate the complete score, we add Kalmy input to the original MEWS. We assign score for each parameter from 0 to 3 presented in Table 2.

The process of the MIFEDSS (Figure 2) starts with the determination of the linguistic variables afforded by the medical experts’ caregivers of ICU. Then, the building of the rules base, with regard to the citied above steps, is realized by the assist of medical expert knowledge. Thereafter, the expert agent calculates membership degree, nonmembership degree, and hesitation margin to determine the degree of risk. Finally, the expert agent transmits the output variable (normal, large, and high) to the doctor agent to provide the suitable treatments to the patient.

Figure 2: The flowchart for MIFEDSS diagnosis process [39].

5. Implementation

In this study we define six input variables performed on a fuzzy logic model by employing MATLAB 2013b software package developed by math works and deployed in the mobile applications [37, 39, 40] by using JFUZZY, a Java-based version of FuzzyLogic; it implements fuzzy control language specification [41].

Figure 3 demonstrates a full number of input variables taken during diagnosis of ICU. A total number of input attributes are a temperature (TEMP), O2S, blood glucose (BG), Kalmy (KAM), systolic blood pressure (SBP), and heart rate (HR). Every input consists of two or three triangular or trapezoidal membership functions. Mamdani system is adopted during analysis due to its capability to describe expertise knowledge in more intuitive and similar to a human like operator. Also, Mamdani type “systems are capable of handling substantial burden” [5]. The output, that is, risk, consists of three triangular membership functions. A total quantity of constructed fuzzy rules is 5400 rules that classify each parameter according to the explanation consulted by a physician.

Figure 3: The IFS inference system (input and output).

This number of rules is calculated using presents the total number of possible rules and presents the number of linguistic parameters for the input fuzzy sets .

Here we present a sample of generated rules in JFUZZY:(i)RULE 64: IF temperature IS low2 AND blood_presure IS low3 AND heart_rate IS low AND o2s IS low3 AND blood_sugar IS low2 AND ka IS low THEN risk IS large;(ii)RULE 76: IF temperature IS low2 AND blood_presure IS low3 AND heart_rate IS low AND o2s IS low2 AND blood_sugar IS low3 AND ka IS low THEN risk IS large;(iii)RULE 91: IF temperature IS low2 AND blood_presure IS low3 AND heart_rate IS low AND o2s IS low1 AND blood_sugar IS low3 AND ka IS low THEN risk IS large;(iv)RULE 94: IF temperature IS low2 AND blood_presure IS low3 AND heart_rate IS low AND o2s IS low1 AND blood_sugar IS low2 AND ka IS low THEN risk IS large;(v)RULE 3053: IF temperature IS normal AND blood_presure IS normal AND heart_rate IS normal AND o2s IS normal AND blood_sugar IS normal AND ka IS normal THEN risk IS normal;(vi)RULE 5232: IF temperature IS high2 AND blood_presure IS high2 AND heart_rate IS high1 AND blood_sugar IS high2 AND ka IS high THEN risk IS high;(vii)RULE 5235: IF temperature IS high2 AND blood_presure IS high2 AND heart_rate IS high1 AND blood_sugar IS high3 AND ka IS high THEN risk IS high;(viii)RULE 5247: IF temperature IS high2 AND blood_presure IS high2 AND heart_rate IS high1 AND blood_sugar IS high2 AND ka IS high THEN risk IS high;(ix)RULE 5262: IF temperature IS high2 AND blood_presure IS high2 AND heart_rate IS high1 AND blood_sugar IS high2 AND ka IS high THEN risk IS high.

Figure 4 illustrates membership plot for Kalmy (KAM) consisting of three membership function values (Hypokalmy, Normal, and Hyperkalmy, Table 3).

Table 3: The range of Kalmy input parameter.
Figure 4: The range of Kalmy parameter.

In temperature (TEMP) input variable, we allocate 3 linguist variables (low 2, normal, and high 2) and for O2S input that presents the oxygen saturation. In this parameter, we have four fuzz sets.

For heart rate (HR) based on the MEWS this parameter presents six fuzzy sets (bradycardy, low, normal, high 1, high 2, and tackcardy) (Table 4 and Figure 5).

Table 4: The range of heart rate input parameter.
Table 5: The output variable (risk) ranges.
Figure 5: The range of heart rate parameter.

The output stage degree of risk is expressed by fuzzy linguistic values such as normal, large, and high as shown in Figure 6. It consists of three triangular membership functions which varies from 0 to 15.

Figure 6: The triangular risk membership functions.

6. Results and Discussion

The validation of the prototype is based on the comparison between the risk calculated by MIFEDSS and the risk presented by the MEWS score. The experimentation presented here (Figure 7) includes 16 patients’ data obtained from the ICU in Polyclinic ESSALEMA, Sfax. Out of the 16 cases, 4 were normal cases, who had normal values, 7 were patients suffering from moderate risk, and the remaining were having severe risk.

Figure 7: Column chart for MEWS score and the MIFEDSS.

In ICU we obtained a satisfactory result, with 100% accuracy in risk linguistic variable values. The results obtained match with the expert’s opinion.

This IFS did perform better than the MEWS method; nevertheless the evaluation performed has led to substantial improvement of the prototype by adding new input parameter like age and annury input in order to increase the chances of successful treatment.

Derived from the experimental results, the proposed method is robust compared to usual MEWS methods in terms of sensitivity, specificity, and accuracy. IFS has shown remarkable improvements in decision-making abilities over typical MEWS score and typical FL, by adding , , and .

This study was undertaken with an aim to design an agent expert system for the diagnosis of risk level in ICU. The results obtained from the prototype disclose that the diagnostic system is giving expected results and its efficiency has been approved by three specialist doctors in Polyclinic ESSALEMA, Sfax. The developed prototype was not intended to replace the expert doctor; however it can be used to support and help the specialist in diagnosing and forecasting patient’s health status. Thus we conclude that studies involving the use of IFS in medical diagnostic are highly assuring for the future according to the existing system.

To date, the results are very encouraging. The generality of the proposed approach presented in Figure 2 suggests its suitability for a diversity of medical decision system in order to assist inexperienced physicians in arriving at final diagnosis of other illnesses more proficiently and competently.

To evaluate our solution, we have proposed to 15 participants a questionnaire-based survey after a training session; the summary of usability/readability approval evaluation results is presented in [37]. In terms of usability, 65% of the interviewees judged that the system was “good.” As for readability assessment, 62% of the interviewees considered that the system was “good.” The evaluation results are suitable and induce us to keep applying the IFS system in other healthcare domain. The evaluation is based on subjective options: good, average, and poor. Yet it is useful to build hybrid evaluation system (objective and subjective measures). Even though we are only on beginning stage, we assume that the combination of the above measures evaluation can ensures a useful model for the analysis of the prototype.

7. Conclusion

Intensive Care Unit (ICU) is a complex healthcare environment especially in diagnostic tasks, when we recurrently deal with inaccurate information (accessible information is occasionally vague, inadequate, or incorrect). Therefore, IFS has been found to be practical to deal with ambiguity. In this regard, decision support system and artificial intelligence using IFS techniques can help us to handle this complexity in a harmless, successful, and proficient way. In this paper, we describe a system for detecting patient’s degree of risk in Intensive Care Unit. The proof of concept prototype is based on intuitionistic fuzzy sets to model the natural uncertainty in making healthcare decisions, which is integrated into a multiagent system. The system has been used as an implemented real life application, and some early results are described in the paper.

Accordingly, we assume that IFS is likely to be of great avail to healthcare domain. Indeed, in future research, we will reflect on other medical applications of this approach and deploy the prototype, not only in Polyclinic ESSALEMA, but also in other healthcare environments to evaluate tolerability and performance of the applications. The MIFEDSS prototype was designed and developed for ICU. Nevertheless, the obtained method may be simply matched and applied to further similar medical decision system. Also, we plan to make hybrid evaluation in order to evaluate the system by both subjective and objective features.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


The authors would like to acknowledge the financial support of this work by grants from the General Direction of Scientific Research and Technological Renovation (DGRSRT), Tunisia, under the ARUB Program LR11ES48. Special thanks are due to the medical staff of Polyclinic ESSALEMA, Sfax, Tunisia. The implementation and evaluation cannot be done without their dedication.


  1. F. Shaddel, V. Khosla, and S. Banerjee, “Effects of introducing MEWS on nursing staff in mental health inpatient settings,” Progress in Neurology and Psychiatry, vol. 18, no. 2, pp. 24–27, 2014. View at Publisher · View at Google Scholar
  2. S. Kar and D. D. Majumder, “An investigative study on early diagnosis of breast cancer using a new approach of mathematical shape theory and neuro-fuzzy classification system,” International Journal of Fuzzy Systems, vol. 18, no. 3, pp. 349–366, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. E. Otto, C. Semotok, J. Andrysek, and O. Basir, “An intelligent diabetes software prototype: Predicting blood glucose levels and recommending regimen changes,” Diabetes Technology and Therapeutics, vol. 2, no. 4, pp. 569–576, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Shereck and F. Jabur, Implementation Of A Fuzzy Logic Based, Expert System To Control Insulin-Pump Doses, Mcgill University, Ece Department, Computer Architecture Lab, 2005.
  5. N. Walia, H. Singh, and A. Sharma, “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey,” International Journal of Computer Applications, vol. 123, no. 13, pp. 32–38, 2015. View at Publisher · View at Google Scholar
  6. B. Cosenza, “Off-line control of the postprandial glycemia in type 1 diabetes patients by a fuzzy logic decision support,” Expert Systems with Applications, vol. 39, no. 12, pp. 10693–10699, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Adeli and M. Nashat, “A Fuzzy Expert System for Heart Disease Diagnosis,” in Proceedings of the International Multi Conference of Engineers and computer scientists, vol. 1, 2010.
  8. Y. Qu, C. Shang, Q. Shen, N. Mac Parthaláin, and W. Wu, “Kernel-based fuzzy-rough nearest-neighbour classification for mammographic risk analysis,” International Journal of Fuzzy Systems, vol. 17, no. 3, pp. 471–483, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  9. A. Jayachandran and G. Kharmega Sundararaj, “Abnormality segmentation and classification of multi-class brain tumor in MR images using fuzzy logic-based hybrid kernel SVM,” International Journal of Fuzzy Systems, vol. 17, no. 3, pp. 434–443, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  10. B. Sun, W. Ma, and X. Chen, “Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision-making,” Expert Systems, vol. 32, no. 4, pp. 507–521, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. K. T. Atanassov, Intuitionistic fuzzy logics, vol. 351 of Studies in Fuzziness and Soft Computing, Springer, Cham, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  12. J. Y. Ahn, K. S. Han, S. Y. Oh, and C. D. Lee, “An application of interval-valued intuitionistic fuzzy sets for medical diagnosis of headache,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 5 B, pp. 2755–2762, 2011. View at Google Scholar · View at Scopus
  13. E. Szmidt and J. Kacprzyk, “Intuitionistic Fuzzy Sets in Some Medical Applications,” in 5th International Conference on IFSs, vol. 7 of NIFS, pp. 58–64, 2001.
  14. E. Szmidt and J. Kacprzyk, “An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis,” in Recent Advances in Intelligent Paradigms and Applications, A. Abraham, L. Jain, and J. Kacprzyk, Eds., pp. 57–70, Springer, Berlin, Germany, 2002. View at Google Scholar
  15. E. Szmidt and J. Kacprzyk, “A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning,” in 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, pp. 388–393, pol, June 2004. View at Scopus
  16. T. Pathinathan, A. Jon, and P. Ilavarasi, “An Application of Interval Valued Intuitionistic Fuzzy Sets in Medical Diagnosis Using Logical Operators,” International Journal of Computing Algorithm, vol. 3, pp. 495–498, 2014. View at Google Scholar
  17. M. Mohammed, “Medical diagnosis via interval valued intuitionistic fuzzy sets,” Annals of Fuzzy Mathematics and Informatics, vol. 6, no. 2, pp. 245–249, 2012. View at Google Scholar
  18. B. Chetia and P. K. Das, “An Application of Interval-Valued Fuzzy Soft Sets in Medical Diagnosis,” International journal Contemp. Math. Sciences, vol. 5, no. 38, pp. 1887–1894, 2010. View at Google Scholar
  19. S. K. De, R. Biswas, and A. R. Roy, “An application of intuitionistic fuzzy sets in medical diagnosis,” Fuzzy Sets and Systems, vol. 117, no. 2, pp. 209–213, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Elie, “Solutions in composite fuzzy relation equation. Application to Medical diagnosis in Brouwerian Logic,” in Fuzzy Automata and Decision Process, Gupta M. M., Saridis G. N., and Gaines B. R., Eds., Elsevier, North-Holland, Netherlands, 1977. View at Google Scholar
  21. G. Çuvalcıoğlu and S. Mercan, “Application of Weak Intuitionistic Fuzzy Sets for Medical Diagnosis,” Adv. Studies in Contemp. Math, vol. 9, no. 2, 2004. View at Google Scholar
  22. L. Todorova, K. Atanassov, S. Hadjitodorov, and P. Vassilev, “On an intuitionistic fuzzy approach for decision making in medicine. Part 2,” Int. Journal Bioautomation, vol. 7, pp. 64–69, 2007. View at Google Scholar
  23. K.-C. Hung, “Medical pattern recognition: Applying an improved Intuitionistic fuzzy Cross-entropy approach,” Advances in Fuzzy Systems, vol. 2012, Article ID 863549, 6 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Ye, “Cosine similarity measures for intuitionistic fuzzy sets and their applications,” Mathematical and Computer Modelling, vol. 53, no. 1-2, pp. 91–97, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  25. B. Li, H. Zhang, and Y. Li, “The molds of intuitionistic fuzzy value and their applications,” International Journal of Fuzzy Systems, vol. 18, no. 2, pp. 284–298, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  26. H. Davarzani and M. A. Khorheh, “A novel application of intuitionistic fuzzy sets theory in medical science: Bacillus colonies recognition,” Artificial Intelligence Research, vol. 2, no. 2, 2013. View at Publisher · View at Google Scholar
  27. V. Khatibi and G. A. Montazer, “Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition,” Artificial Intelligence in Medicine, vol. 47, no. 1, pp. 43–52, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Chaira, “Intuitionistic fuzzy segmentation of medical images,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 6, pp. 1430–1436, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Chaira, “Intuitionistic fuzzy set approach for color region extraction,” Journal of Scientific and Industrial Research, vol. 69, no. 6, pp. 426–432, 2010. View at Google Scholar · View at Scopus
  30. T. Chaira, “A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 1711–1717, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Jemal, Z. Kechaou, M. B. Ayed, and A. M. Alimi, “A Multi Agent System for Hospital Organization,” International Journal of Machine Learning and Computing, vol. 5, no. 1, pp. 51–56, 2015. View at Publisher · View at Google Scholar
  32. H. Jemal, Z. Kechaou, and M. Ben Ayed, “Swarm intelligence and multi agent system in healthcare,” in 6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014, pp. 423–427, tun, August 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Jemal, Z. Kechaou, and M. Ben Ayed, “Towards a Medical Intensive Care Unit Decision Support System Based on Intuitionistic Fuzzy Logic,” in Intelligent Systems Design and Applications, vol. 557 of Advances in Intelligent Systems and Computing, pp. 602–611, Springer International Publishing, Cham, 2017. View at Publisher · View at Google Scholar
  34. K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87–96, 1986. View at Publisher · View at Google Scholar · View at Scopus
  35. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Edward Samuel and M. Balamurugan, “Fuzzy max-min composition technique in medical diagnosis,” Applied Mathematical Sciences, vol. 6, no. 33-36, pp. 1741–1746, 2012. View at Google Scholar · View at Scopus
  37. J. Hanen, Z. Kechaou, and M. B. Ayed, “An enhanced healthcare system in mobile cloud computing environment,” Vietnam Journal of Computer Science, vol. 3, no. 4, pp. 267–277, 2016. View at Publisher · View at Google Scholar
  38. C. Stenhouse, S. Coates, M. Tivey, P. Allsop, and T. Parker, “Prospective evaluation of a modified Early Warning Score to aid earlier detection of patients developing critical illness on a general surgical ward,” British Journal of Anaesthesia, vol. 84, no. 5, pp. 663–663, 2000. View at Publisher · View at Google Scholar
  39. H. Jemal, Z. Kechaou, M. Ben Ayed, and A. M. Alimi, “Cloud computing and mobile devices based system for healthcare application,” in IEEE International Symposium on Technology and Society, ISTAS 2015, irl, November 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. H. emal, Z. Kechaou, M. Ben Ayed, and A. M. Alimi, “Mobile Cloud Computing in Healthcare System,” in In Proceedings of 7th International Conference on Computational Collective Intelligence: Semantic Web, Social Networks & Multiagent Systems, vol. 9330 of Lecture Notes in Computer Science, pp. 408–417, Madrid, Spain, September 2015.