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. | Survey to assess potential risks. | Identified the most critical risk. |
| AlZu’bi et al. [25] | 3D fuzzyC-means algorithm. | 3D medical image segmentation. | Parallel implementation to be 5× faster than the sequential version. |
| Ozsahin et al. [23] | FuzzyPROMETHEE And fuzzy MCDM. | Solid-state detectors in medical imaging | Most suitable semiconductor on basis of detectors. |
| Masood et al. [22] | Hybrid hierarchical fuzzy group decision making. | Selection of conceptual loudspeaker prototype under sustainability issues. | Optimal conceptual prototype design among 4 alternatives. |
| Part B: Service quality and risk assessment typically in chronic diseases | |||
| Vidhya and Shanmugalakshmi [29] | Big Data and neuro fuzzy-based method | Analysis of multiple diseases using an adaptive neuro-fuzzy inference system. | Determined the entropy of the CFI count. |
| Akinnuwesi et al. [28] | Hybridization of fuzzy-Logic and cognitive mapping techniques. | Decision support system for diagnosing rheumatic–musculoskeletal disease. | 87% accuracy, 90% sensitivity, and 80% specificity. |
| La Fata et al. [26] | Fuzzy ELECTRE III. | Evaluated the service quality in public healthcare. | Significant service attributes factors. |
| Samiei et al. [27] | Neuro-fuzzy inference system. | Risk factors of low back pain. | Identified four major risk factors to low back pain. |
| Part C: Decision making and the role of operations research | |||
| Vaishnavi and Suresh [30] | Fuzzy readiness and performance importance indices. | To implement agility in healthcare systems. | Continuation of assessment readiness helps to improve readiness. |
| Detcharat Sumrit [31] | Fuzzy MCDM approach. | Supplier selection for vendor-managed inventory in healthcare. | Institutional trust, information sharing, and technology as major evaluation criteria. |
| Rajput et al. [33] | Fuzzy signed distance technique. |
Optimization of fuzzy EOQ model in healthcare industries.
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