Aggregation operators on group-based generalized q-rung orthopair fuzzy N-soft sets and applications in solar panel evaluation.

Every problem in decision-making has a solution when the information that is available is properly and precisely modeled. This study focuses on non-binary data from N-soft sets and q-rung orthopair fuzzy values, referred to as group-based generalized q-rung orthopair fuzzy N-soft sets (GGq-ROFNSSs). The GGq-ROFNSSs model provides information simultaneously on numerous competing criteria, alternatives, sub-alternatives, and data summarization. We introduce properties of GGq-ROFNSSs such as distinct inclusion features of GGq-ROFNSSs, weak complements of the GGq-ROFNSS, top weak complements the GGq-ROFNSS, bottom weak complements the GGq-ROFNSS. We provide the notion of GGq-ROFNSWA and GGq-ROFNSWG operators as well as their idempotency, monotonicity, and boundedness features. The notion of GGq-ROFNSSs requires a sound methodology of multiple criteria decision making (MCDM) since GGq-ROFNSS combines numerous elements of complex decision-making. We provide a MCDM methodology for the GGq-ROFNSWA and GGq-ROFNSWG operators and depict it in a flowchart. The selection of solar panels for a city is a difficult procedure because it depends on several components such as environment, where the area is located, what kinds of needs are being met, etc. We find a solution to the problem of selecting a suitable solar panel for a city with their underlying characteristics. Finally, we provide a comparison of the suggested method with other techniques to demonstrate its advantages.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets.

Background: Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets. Methods: According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared. Results: Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients. Conclusions: Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

Bayesian federated inference for estimating statistical models based on non-shared multicenter data sets.

Identifying predictive factors for an outcome of interest via a multivariable analysis is often difficult when the data set is small. Combining data from different medical centers into a single (larger) database would alleviate this problem, but is in practice challenging due to regulatory and logistic problems. Federated learning (FL) is a machine learning approach that aims to construct from local inferences in separate data centers what would have been inferred had the data sets been merged. It seeks to harvest the statistical power of larger data sets without actually creating them. The FL strategy is not always efficient and precise. Therefore, in this paper we refine and implement an alternative Bayesian federated inference (BFI) framework for multicenter data with the same aim as FL. The BFI framework is designed to cope with small data sets by inferring locally not only the optimal parameter values, but also additional features of the posterior parameter distribution, capturing information beyond what is used in FL. BFI has the additional benefit that a single inference cycle across the centers is sufficient, whereas FL needs multiple cycles. We quantify the performance of the proposed methodology on simulated and real life data.

STK3 higher expression association with clinical characteristics in intrinsic subtypes of breast cancer invasive ductal carcinoma patients.

PURPOSE: STK3 has a central role in maintaining cell homeostasis, proliferation, growth, and apoptosis. Previously, we investigated the functional link between STK3/MST2, and estrogen receptor in MCF-7 breast cancer cells. To expand the investigation, this study evaluated STK3's higher expression and associated genes in breast cancer intrinsic subtypes using publicly available data. METHODS: The relationship between clinical pathologic features and STK3 high expression was analyzed using descriptive and multivariate analysis. RESULTS: Increased STK3 expression in breast cancer was significantly associated with higher pathological cancer stages, and a different expression level was observed in the intrinsic subtypes of breast cancer. Kaplan-Meier analysis showed that breast cancer with high STK3 had a lower survival rate in IDC patients than that with low STK3 expression (p < 0.05). The multivariate analysis unveiled a strong correlation between STK3 expression and the survival rate among IDC patients, demonstrating hazard ratios for lower expression. In the TCGA dataset, the hazard ratio was 0.56 (95% CI 0.34-0.94, p = 0.029) for patients deceased with tumor, and 0.62 (95% CI 0.42-0.92, p = 0.017) for all deceased patients. Additionally, in the METABRIC dataset, the hazard ratio was 0.76 (95% CI 0.64-0.91, p = 0.003) for those deceased with tumor. From GSEA outcomes 7 gene sets were selected based on statistical significance (FDR < 0.25 and p < 0.05). Weighted Sum model (WSM) derived top 5% genes also have higher expression in basal and lower in luminal A in association with STK3. CONCLUSION: By introducing a novel bioinformatics approach that combines GSEA and WSM, the study successfully identified the top 5% of genes associated with higher expression of STK3.

An extension of the best-worst method based on the spherical fuzzy sets for multi-criteria decision-making.

The ambiguous information in multi-criteria decision-making (MCDM) and the vagueness of decision-makers for qualitative judgments necessitate accurate tools to overcome uncertainties and generate reliable solutions. As one of the latest and most powerful MCDM methods for obtaining criteria weight, the best-worst method (BWM) has been developed. Compared to other MCDM methods, such as the analytic hierarchy process, the BWM requires fewer pairwise comparisons and produces more consistent results. Consequently, the main objective of this study is to develop an extension of BWM using spherical fuzzy sets (SFS) to address MCDM problems under uncertain conditions. Hesitancy, non-membership, and membership degrees are three-dimensional functions included in the SFS. The presence of three defined degrees allows decision-makers to express their judgments more accurately. An optimization model based on nonlinear constraints is used to determine optimal spherical fuzzy weight coefficients (SF-BWM). Additionally, a consistency ratio is proposed for the SF-BWM to assess the reliability of the proposed method in comparison to other versions of BWM. SF-BWM is examined using two numerical decision-making problems. The results show that the proposed method based on the SF-BWM provided the criteria weights with the same priority as the BWM and fuzzy BWM. However, there are differences in the criteria weight values based on the SF-BWM that indicate the accuracy and reliability of the obtained results. The main advantage of using SF-BWM is providing a better consistency ratio. Based on the comparative analysis, the consistency ratio obtained for SF-BWM is threefold better than the BWM and fuzzy BWM methods, which leads to more accurate results than BWM and fuzzy BWM.

Synthesis of temporomandibular disorders management intervention outcomes for development of core outcome sets: A systematic review.

INTRODUCTION: The selection of appropriate outcomes in clinical trials and systematic reviews is a crucial factor in determining the results that are useful, reliable, and relevant for both patients and healthcare professionals. Clinicians and researchers have been encouraged to develop and apply core outcome sets (COS) to minimise the discrepancy between studies. AIM: This systematic review is the first phase of the COS development project for clinical trials in temporomandibular disorders (COS-TMD). It aims to identify and synthesise the outcomes used in the randomised controlled trials (RCT) that evaluated the effectiveness of interventions used in TMD management. MATERIALS AND METHODS: An electronic search was performed in several databases: MEDLINE (via PubMed), Scopus, Web of Science, Cochrane Library and EMBASE. The eligibility criteria comprised RCT that applied any intervention to treat temporomandibular joint disorders or masticatory muscle disorders. The identified outcomes were categorised according to domains of the Initiative on Methods, Measurement and Pain Assessment in Clinical Trials (IMMPACT). RESULTS: The electronic search resulted in 1606 studies. After removing duplicates and applying the eligibility criteria, 106 RCT were included. A total of 43 studies evaluated masticatory muscle disorders, 27 evaluated temporomandibular joint disorders, and 36 analysed mixed TMD. CONCLUSIONS: The evaluation showed significant variability in the types of outcomes and their measurement instruments. In addition, some domains such as physical and emotional functioning, participant ratings of global improvement and adverse events have been neglected when determining the effectiveness of treatments for TMD.

Extension of simple multi-attribute rating technique in uncertainty environment for 5G industry evaluation: Egyptian new administrative capital as a case study.

As is well-known, multicriteria decision-making (MCDM) approaches can aid decision-makers in identifying the optimal alternative based on predetermined criteria. However, it is a big challenge to apply this approach in complex applications such as 5th generation (5G) industry assessment because criteria are challenging and trade-offs between them are hard. Also, assessment of the 5G industry involve strong uncertainty. So, this study is the first to evaluate the 5G industry using a new neutrosophic simple multi-attribute rating technique (N-SMART). Since neutrosophic set considers truth-degree, indeterminacy-degree, and falsity-degree, it is a more accurate instrument for evaluating uncertainty. The 5G assessment issue exemplifies the validity and great performance of our proposed method as: (1) its ability to deal with uncertainty phenomena; (2) its simplicity; and (3) its enhanced capacity to discern alternatives. Also, by considering the 5G service provided in the Egyptian New Administrative capital as a case study, the results showed that Ericsson 5G is the best choice and Nokia 5G is the worst choice.

A novel IFN-CSM-CoCoSo approach for multiple-attribute group decision-making with intuitionistic fuzzy sets: An application in assessing corporate social responsibility performance.

With the rapid growth of the economy, enterprises have encountered a series of problems while pursuing economic benefits, such as food safety and environmental pollution issues, resource shortages and energy consumption issues, which affect the sustainable development of enterprises. Establishing a corporate performance evaluation system from the perspective of social responsibility, based on stakeholder theory and the importance of overall goals reflected in the weight of social responsibility indicators, is a very effective measure to achieve corporate social responsibility (CSR) goals through CSR motivation and stakeholders. The performance evaluation of CSR from the perspective of environmental accounting is a MAGDM. Recently, the CoCoSo technique and cosine similarity measure (CSM) technique was utilized to conduct the MAGDM. The intuitionistic fuzzy sets (IFSs) are utilized as a technique for conducting uncertain information during the performance evaluation of CSR from the perspective of environmental accounting. In this study, the intuitionistic fuzzy CoCoSo based on the CSM (IFN-CSM-CoCoSo) technique is built for MAGDM with IFSs. Finally, a numerical example for performance evaluation of CSR from the perspective of environmental accounting is conducted to verify the IFN-CSM-CoCoSo technique.

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety.

Pythagorean cubic fuzzy sets represent an advancement beyond conventional interval-valued Pythagorean sets, integrating the principles of Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets. Given the critical significance of distance measures in real-world decision-making and pattern recognition tasks, it is noteworthy that there exists a notable gap in the literature regarding distance measures specifically tailored for Pythagorean cubic fuzzy sets. The objectives of this paper are:•To define novel generalized distance measures between Pythagorean cubic fuzzy sets (PCFSs) to tackle intricate decision-making challenges.•These novel distance measures are undergoing testing on a real-world scenario concerning the management of anxiety and depression to evaluate their effectiveness and practical application.•We have illustrated the boundedness and nonlinear characteristics inherent in these distance measures. In addition, we conduct comparative analyses with existing approaches to validate the proposed methodology, thereby providing insights into its advantages and potential applications.

Sensory Profile-2 in Autism Spectrum Disorder: An Analysis within the International Classification of Functioning, Disability and Health Framework.

Autism spectrum disorder (ASD) is characterized by impairments in many functional areas requiring long-term interventions to promote autonomy. This study aims to map The Sensory Profile™ 2 (SP-2), one of the most widely used assessment tools in children with ASD, with the International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY), developed by the World Health Organization (WHO). This will allow the identification of the functional dimensions covered by this instrument and the comparison with the ICF shortlist proposed for autism (ICF Core Set [ICF-CS]). The deductive content analysis described in the ICF Linking Rules was followed, along with a systematized process including statistical and reasoning techniques that could contribute to the improvement of ICF linking studies (Cohen's Kappa and percentage of agreement). 218 codes were identified, 71% of them were codes related to the body functions chapters, mainly linked to perceptual functions (b160), emotional functions (b152), and temperament and personality functions (b126). Concerning activities and participation chapters (29%) the most frequently used codes were: focusing attention (d160), carrying out daily routine (d230), and walking (d450). Even though the SP-2 items do not assess most of the functional features regarded as essential in the ASD ICF-CS, SP-2 encompasses a majority of problems concerning body functions. This instrument may be considered as part of a multidimensional assessment approach, to complement other sources that are more likely to assess activity and participation dimensions and guide a functional intervention.

Shared manufacturing service evaluation based on intuitionistic fuzzy VIKOR.

With the rise of the concept of sharing economy, the shared manufacturing model has gained widespread attention. The VIKOR shared manufacturing service evaluation approach is presented based on an intuitionistic fuzzy environment, which enables users to filter out acceptable shared manufacturing services from a wide pool of shared manufacturing services with similar functional qualities. Firstly, considering the QOS multi-indicator comprehensive evaluation of services by multiple stakeholders under the fundamental characteristics of shared manufacturing, the QOS evaluation index system is built from the two aspects of online and offline, which includes 2 first-level indicators and 10 second-level indicators. The paper also constructs a service recommendation model considering supply and demand constraints. Secondly, the intuitionistic fuzzy numbers are introduced to define the non-quantitative indexes, and the G1-method and variable-precision rough set theory are used for the assignment, and the maximum entropy theory is utilized to integrate the assignment method to obtain the combination weights. Thirdly, the VIKOR method based on intuitionistic fuzzy sets is used to evaluate and rank the shared manufacturing candidate services, in which the combined benefits and minimum regret values of the groups are solved based on the intuitionistic fuzzy number similarity. Finally, the reliability and feasibility of the algorithm are verified with a real case.

Hesitant and uncertain linguistics based executive decision making using risk and regret aversion: Methods, implementation and analysis.

Presence of globally-affecting issues, such as the recent COVID-19 pandemic is a major factor impacting the operation of services provided by high-stake companies. These factors create huge hindrances in the regular and proper operations of companies in staying relevant in market while catering to the services they provide. In such cases, in order to maintain and achieve their internal goals should any possible losses that the grave situation might incur, relevant experts within these firms must arrive at optimal decisions taking into account human cognition as well as all possibilities of risk and regrets. A suitable regret theory based linguistic decision-making model called THREAD which computes with inherent hesitancy using interval type-2 fuzzy sets (IT2 FS) and hesitant fuzzy linguistic term sets-based techniques is introduced in this paper.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources.

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

Repeated-sprint sets test: a new method for evaluating and forecasting fitness in elite young male soccer players.

The objective of the current study was to explore the correlation between repeated sprint sets (RSS) ability and several physical attributes, including maximum sprint speed, maximal aerobic speed, maximal anaerobic speed, aerobic capacity, and explosive strength. Moreover, the aim was to assess the suitability of RSS as a comprehensive evaluation tool for physical qualities and to determine which physical field tests most accurately predict RSS in elite young male soccer players. A total of thirty-two young elite male soccer players (mean age 14.6 ± 0.3 years; predicted years from peak height velocity (PHV): - 0.4 ± 0.3; years in training: 3.7 ± 0.5) voluntarily participated in the study. The players participated in eight consecutive specific physical tests, with a minimum 72-h recovery between each session to minimize the impact of fatigue during the second trial. The participants completed the tests in the following order: RSS test, Vam-Eval test, a constant velocity test performed until exhaustion at 100% of vVO2max (tlim100), 20-m Multi-Stage Shuttle Run test (VMSRT), Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1), Maximal Anaerobic Shuttle Running Test (VMASRT), Maximal Sprinting Speed Test (20-m flying sprint), Countermovement Jump (CMJ), and Standing Long Jump test (SLJ). The results of the study showed that there were very large negative correlations between tlim100 and SST (sum of sprint times), and large negative correlations between Yo-Yo IR1, Vam-Eval, and SST during RSS in young elite male soccer players (p < 0.05). Additionally, VMASRT and SLJ demonstrated a moderate negative correlation with SST (p < 0.05). In contrast, significant positive correlations were found between 20-m flying sprint and the SST (p < 0.05). According to the stepwise multiple linear regression analysis, the primary predictors of SST, ranked by importance, were tlim100 and Yo-Yo IR1. These two predictors collectively accounted for 72% of the variance in players' SST (p < 0.0001). Due to the importance of aerobic capacity and short repeated accelerations/sprint sets for overall competitive performance in soccer, in conclusion, our results suggest that elite young male soccer players should perform both high intensity interval training and aeorobic capactity exercises as part of soccer training if the primary outcome is to improve repeated sprint ability performance.

Cosine and cotangent similarity measures for intuitionistic fuzzy hypersoft sets with application in MADM problem.

Intuitionistic fuzzy hypersoft sets (IFHSSs) are a novel model that is projected to address the limitations of Intuitionistic fuzzy soft sets (IFSSs) regarding the entitlement of a multi-argument domain for the approximation of parameters under consideration. It is more flexible and reliable as it considers the further classification of parameters into their relevant parametric valued sets. In this paper, we proposed some trigonometric (cosine and cotangent) similarity measures and their weighted trigonometric similarity measures (SMs). Trigonometric Similarity measures (SMs) for intuitionistic fuzzy hypersoft sets (IFHSSs) are significantly implied to check the similarity measures and help to determine the similarity between different factors. Also, in order to evaluate the validity of the significant study and apply the results to a daily life problem. We use them to solve problems involving the selection of renewable energy sources. According to several technical contributing factors, the analysis identifies the ideal location for the implementation of the energy production units. Future case studies with many features and additional bifurcation along with multiple decision-makers can use the suggested methodologies. Also, several existing structures, such as fuzzy, Pythagorean fuzzy, Neutrosophic theories, etc., can be utilized with the suggested method.

Harmonising outcome measurement for child focused domestic abuse interventions. Reflections on the development and implementation of a core outcome set.

There is appetite in the UK to better measure the impact of domestic violence and abuse (DVA) interventions on children. The spread of outcomes-based commissioning means outcome measurement is no longer just the territory of academic researchers but is now firmly within the purview of practitioners and policy makers. However, outcomes measured in trials only partially represent the views of those delivering and using services with respect to how success should be defined and captured. Even within trials there is huge inconsistency in the definition and measurement of important endpoints. This yields a body of evidence that is difficult to make sense of, defeating the ends for which it was produced - to improve the response to children and families who have experienced abuse. Development of Core Outcome Sets (COS) is seen as a solution to this problem, by establishing consensus across key stakeholder groups regarding a minimum standard for outcome measurement in trials, and increasingly in service delivery contexts. To date COS development has addressed outcomes relating to health conditions or interventions, with limited application to public health challenges. We reflect on our efforts to develop a COS to evaluate psychosocial interventions for children and families experiencing DVA. We highlight the value of COS development as a mechanism for improving evidence quality and the response to families experiencing abuse. Finally, we make recommendations to researchers and COS guideline developers to support this broader application of COS methodology.

Thickness and design features of clinical cranial implants-what should automated methods strive to replicate?

PURPOSE: New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use. METHODS: Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified. RESULTS: The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull. CONCLUSIONS: For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.

Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment.

The COVID-19 pandemic has caused a surge in essential medical supplies usage, leading to a notable increase in medical waste generation. Consequently, extensive research has focused on sustainable disposal methods to handle used medical equipment safely. Given the necessity to evaluate these methods considering qualitative and quantitative criteria, this falls within the realm of multi-criteria decision-making (MCDM). This study introduces a framework for selecting the most suitable medical waste treatment methods, taking into account economic, technological, environmental, and social aspects. Sixteen criteria were assessed using the Fuzzy Preference Selection Index (F-PSI) to determine the optimal waste disposal approach. Additionally, the Fuzzy Compromise Ranking of Alternatives from Distance to Ideal Solution (F-CRADIS) method was employed to evaluate nine technologies for medical waste disposal. Notably, disinfection efficiency emerged as the most crucial criterion, with autoclaving identified as the preferred method for medical waste treatment. A practical case study conducted in Sivas, Turkey, validates the feasibility of these strategies. Multiple sensitivity analyses were performed to ensure the stability and reliability of the proposed approach.