2.5.3 Location of Privacy
Area protection is tied in with ensuring the security of the physical area of the residents. The LBS of our application model involves area protection issues. When residents attempt to get ideal courses, they send their area to CCC and permit the LBS supplier to follow them. A few strategies have been proposed to secure area protection. Their point is to give a twisted area that forestalls the supplier from the following clients. The creators proposed a blend zone—another development roused by unknown correspondence methods—along with measurements for evaluating client namelessness. The study proposed the thought of much of the time changing pen names every client, which would lessen the opportunity that an assailant could gather enough history on a casualty to construe their propensities or personality. Creators propose a system that empowers an architect to locate the ideal area protection safeguarding component (LPPM) for an LBS thinking about limitations of the client’s administration quality, and an enemy dependent on the calculation of ideal induction. LocX [24] gives altogether improved area protection without including vulnerability into question results or depending on solid suspicions about server security. Specifically, LocX applies secure client explicit, separation protecting direction changes to all area information imparted to the server.
2.5.4 Footprint Privacy and Owner Privacy
These security issues are identified with the insurance of information gathered by the city framework. Such data can be recovered or derived by lawbreakers. In the s-Health design, contamination levels in the city are gathered by sensors. The city can break down such conditions and afterward, naturally make a choice, for example, resync traffic lights for lessening clog furthermore, contamination in some region of the city. Moreover, electronic wellbeing records of residents are likewise put away by the CCC. Every one of this information can be discharged to outsiders for examination, insights, or information mining, which implies we have to secure delicate data from getting out of hand substances. Sending measurable revelation control (SDC) methods is a viable approach to lessen conceivable protection intrusions that could get from such mining techniques. A strategy that permits information proprietors to produce unexpectedly annoyed duplicates of its information for various trust levels. Thusly, it keeps information excavators from joining duplicates at various trust levels to together remake the first information.
2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems
The world’s population is aging. Age is more than just a number. One single major challenge of policymakers across geographies is to arrange efficient healthcare services to upgrade the living standards of the aging population. In recent years, there has been a significant surge in the number of patients with various chronic diseases associated with a variety of risk factors requiring long-term treatment. Under these complexities, the decisions by caregivers cover the critical ripple effects [8]. So, a tiny mistake by them can be irrecoverable and fatal for patients [1]. However, various stakeholders of healthcare industries, including managers and legislators, hardly furnish precise and crisp information. The inherent uncertainty in the data brings fuzzy set theory into the forefront. Whereas the fuzzy set theory has been widely applied to deliver the acceptable solutions to diverse healthcare and medical issues, researchers are employing several recent tools, like type 2 fuzzy set, intuitionistic fuzzy set, and many more for higher efficiency. Here, the present review briefly focuses on only following three major sub-areas of applications of fuzzy set theory and its derivatives in healthcare and medical problems:
1 A. selection of medical equipment, material, and technology,
2 B. service quality and risk assessment typically in chronic diseases, and
3 C. decision making and the role of operations research.
A. Selection of Medical Equipment, Material, and Technology In recent times, researchers categorized the interrelationship (with several alternatives) among medical types of equipment and materials. So, they could present numerous approaches and methods regarding the assessment and selection of types of equipment, materials, and projects.
In recent years, [3] presented an empirical case study on the robot selection problem by extending the PROMETHEE method under fuzzy environment. Their novel approach included the simultaneous exploration of crisp objective data and fuzzy subjective data. They found how the appropriate robot selection could help to enhance the value of products and thereby resulted in the increased satisfaction of patients, relatives, and caregivers. Around the same time, a study in this area along with potential applications in manufacturing industries was performed in [2]. He extended the classical VIKOR method for robot selection under uncertainty. He employed the interval type-2 fuzzy set to get more degrees of freedom to real-life problems. As well, he analyzed the stability of the proposed method through seven sets of criteria weights and the Spearman correlation coefficient. [4] performed a well-established study by amalgamating two fuzzy-based hierarchal processes, namely fuzzy AHP and fuzzy VIKOR in mobile robot selection. Their study focused on the total ownership of cost as a key parameter in the selection of the robot. Along with some modern technology marvels, like the robotic automation system and Internet of Health Things (IoHT), the modified fuzzy AHP and fuzzy VIKOR methods were applied to determine the ranking of robots and thereby to select the best mobile robot at the hospital pharmacy. Next, [5] found how millions of people received frequent health pieces of advice to lead a healthy life. They noted that while the IoT devices could generate a large volume of data in the healthcare environment, the cloud computing technology could be rewarding for secured storage and accessibility. Additionally, they applied a new systematic approach for the people, who were severely affected with diabetes, by generating the related medical data through some repository dataset and the medical sensors. Their suggested classification algorithm was called the fuzzy rule-based neural classifier that could more effectively diagnose the disease and the severity than classical methods. On the other hand, whereas most researches recognized the hospitals to act as the main sub-section of the healthcare system, they assumed the hospitals at different locations to be at par and homogeneous. However, Omrani et al. [6] studied the non-homogeneous nature of services offered to various patients by the hospitals at different locations. So, they found that these hospitals were unsuitable for comparison. Accordingly, they proposed a clustering technique to deal with a lack of homogeneity among DMUs and thereby to measure the hospitals in different places. Again, [7] addressed the impact of various harmful factors in the information security of healthcare devices. They employed a fuzzy-based symmetrical AHPTOPSIS method. However, they could test the method only at one local hospital software of Varanasi, a city of India. The work by [8] found the drawbacks of type 1 fuzzy set theory and used the finite interval-valued type 2 Gaussian fuzzy number as a powerful tool to measure uncertainty in healthcare problems. This could solve a real economic evaluation of medical device selection problem from the perspectives of clinicians, biomedical engineers, and healthcare investors. Part A of Table 2.1 lists some very recent articles in this area of research. This way, numerous researchers have put their best effort to tackle the uncertainty intrinsic to healthcare and medical problems.
Table 2.1 Very recent articles focusing on applications of fuzzy set theory in healthcare and medical problems.
| Author(s) | Approach | Purpose of the study | Outcome |
| Part A: Selection of medical equipment, material, and technology | |||
| Moreno-Cabezali and Fernandez-Crehuet [24] | Fuzzy logic in risk assessment. |
| |