Cell preservation is among the most crucial methodologies for the long-term storage and usage of cellular material in various fields such as medicine, biotechnology, and scientific research. Different preservation methods have been developed, each with diverse benefits and inherent drawbacks. Selecting the most appropriate method for a particular application, however, becomes a challenge because of the complex and multidimensional nature of the decision-making process. In recent years, multiple-criteria decision-making (MCDM) methods, such as fuzzy Preference Ranking Organization Method for Enrichment Evaluation (fuzzy PROMETHEE), have been deployed to analytically rank and select alternatives based on multiple performance criteria. This study used fuzzy PROMETHEE to assess and comparatively analyze six different approaches for preserving cells by considering fourteen criteria pertinent to the biomedical and food sectors. The analysis showed that vitrification ranked highest in the biomedical context with a net flow value of 0.0015, while freeze-drying was ranked highest for food-related applications with a net flow of 0.0319. These results advance the field of cell preservation by further elucidating the functional mechanisms and limitations of each method used for cell preservation, which may inform the development of new preservation techniques or the refinement of current practices.

Kidney transplantation continues to be the best treatment method for patients with end-stage renal disease; however, it is still a challenge to estimate its survival outcomes due to the complex nature of the kidney itself and its related health conditions. Prediction models provide a reliable basis to improve organ distribution and patient outcomes. Therefore, this study employed multiple linear regression to assess the transplant survival predictive ability based on four categories: patient demographics and clinical characteristics, donor characteristics, organ condition, and a combination of all parameters in relation to kidney transplant survival. A dataset of 1,000 kidney transplant cases was utilized, and four different regression models (one model per category) were developed. The applied algorithms significantly ($\mathbf{p}<0.001$) yielded high predictive power, achieving an$\mathbf{R}^{\mathbf{2}}$of more than$\mathbf{9 5 \%}$, with the overall performance increasing as the number of input parameters increases, whereby the model with a combination of all the 14 variables yielded an$\mathbf{R}^{\mathbf{2}}$of 0.9863 with the lowest MAE and RMSE scores compared to other models. These findings highlight the impact of considering multiple significant clinical variables in transplant survival estimation as well as the importance of perioperative organ assessment and the possibility of widely available tools to support clinical decisionmaking in transplant medicine, thus promoting equitable access to predictive analytics within medical facilities, especially those with limited healthcare settings.

Pancreatic cancer remains one of the deadiest forms of cancer worldwide. The main challenge in pancreatic cancer diagnosis is primarily attributed to the late stage at which it is typically diagnosed. Computed tomography (CT) has been used to provide a concise visualization of the pancreas, however, due to late pancreatic tumor diagnosis, this imaging technique is yet to be highly effective in detecting this malignancy at an early stage. Therefore, this research aims to apply pre-trained deep learning models in classifying CT scan images for the early detection of pancreatic cancer. Residual Network 50 (ResNet50), MSHA, and EfficientNet are the algorithms applied to analyze CT scans and identify cancerous growths in the pancreas. The results show high classification precision, with the highest achieved precision score of $\mathbf{1 0 0. 0 0 \%}$ using MSHA. However, all the applied models had a high performance in detecting pancreatic cancer as they all had scores not less than $\mathbf{9 9 \%}$ in all performance metrics used. These findings emphasize its proficiency in precisely identifying and categorizing pancreatic cancer. This program aims to alleviate the tremendous workload of healthcare systems caused by the large number of medical images analyzed by medical professionals. It specifically can assist radiologists and other specialists in detecting pancreatic tumors by offering a faster and more accurate technique.

Childbirth is a life-giving procedure whereby a whole individual is given an existence though at some points there may be a loss of infants along the journey. Maternal morbidity and death continue to be global issues despite improvements in contemporary obstetrics. This study aims at evaluating vaginal birth, emergency cesarean, elective cesarean, vaginal birth after C-section, vacuum extraction, and forceps delivery techniques considering the selected criteria which are the requirement of medical indication, patient preparation, the use of prophylactic antibiotics, infection rate, mortality rate, safety, prenatal hemorrhage, post-partum hemorrhage, laceration, use of general anesthesia, birth trauma, organ injury, dystocia, fetal distress, time of the procedure, cost, recovery, and the formation of long term scars using fuzzy Preference Ranking Organization Method for Enrichment Evaluations (fuzzy PROMETHEE); a multi-criteria decision-making (MCDM) model. The results of this study showed that vaginal birth was the first preferable ranked technique with a net outranking flow of 0.0773 and forceps delivery, vacuum extraction, vaginal birth after c-section, and emergency cesarean followed, with a net flow of $0.0195,0.0123$, $-0.0129,-0.0422$, respectively. The elective cesarean method was found to be the least preferable technique ranking sixth with a net flow of $\mathbf{- 0 . 0 5 4 0}$. Fuzzy PROMETHEE showed high effectiveness and accuracy in the ranking system. This method will support midwives, obstetricians, and mothers in deciding and weighing the risks associated with possible delivery approaches.

The uncontrollable growth of healthy cells in or around the vulva is known as vulva cancer. The most known risk factor for this type of cancer is the infection of the Human papillomavirus (HPV). Vulva cancer is most likely misdiagnosed as an inflammatory condition that leads to late treatment thus increasing the mortality rate of this type of cancer. Therefore, the current study examines the diagnosis and treatment techniques of vulva cancer considering a list of selected criteria using fuzzy Preference Ranking for Organization Method of Enrichment Evaluation (fuzzy PROMETHEE). The chosen diagnostic alternatives are cystoscopy, colposcopy, biopsy, Positron Emission Tomography (PET), Computed Tomography (CT) scan, and Magnetic Resonance Imaging (MRI) whereas the therapeutic alternatives are lymph node biopsy, vulvectomy, wide local excision, chemotherapy, radiation therapy, immunotherapy, and targeted therapy. In the ranking of the diagnostic methods, punch biopsy ranked first with a net flow of 0.2548 whereas CT scan ranked sixth with a net flow of ${- 0. 2 0 3 6}$. Furthermore, the treatment ranking resulted in lymph node biopsy as the first treatment alternative with a net flow of 0.5466 and immunotherapy as the seventh alternative with a net flow of $-{0}.5388$. The fuzzy PROMETHEE algorithm showed high effectiveness and precision in ranking the proposed alternatives. Thus, the results of this study will assist gynecologists and oncologists in making the appropriate decision for the early detection and treatment of vulva cancer.

The rise of health/medical informatics has created new opportunities for diagnosing different diseases. This is due to its cost-effectiveness and reliable ability to provide clear and understandable information that specialists and policymakers can use. This study aims at screening and predicting clinical lung cancer using a combination of socio-demographic and clinical input factors by implementing hybridized-novel paradigms integrated with multi-model single techniques. Four distinct multimodal single approaches combined with three hybridized novel paradigms were applied to predict lung cancer in a clinical setting. Both the quantitative and graphical performance of the individual methods demonstrate the superior capability of the Gaussian process regression (GPR) model compared to the other three models: Least-square boost (L-Boost), Step-wise-linear regression (SWLR), and Support Vector Regression (SVR). As a result of the inadequate performance and decreased effectiveness of individual procedures, three hybridized novel paradigms were introduced, namely; Stepwise-linear regression- Gaussian process regression (SWLR-GPR), Stepwise-linear regression- Least-square boost (SWLR-L-BOOST) and Stepwise-linear regression- Support Vector Regression (SWLR-SVR) to enhance the predictive abilities of individual methodologies in the clinical prediction of lung cancer by using regression methods. The comparative examination of the hybridized paradigms revealed that SWLR-GPR outperforms all other hybridized paradigms and single approaches in predicting lung cancer. Based on the results of this research and the efficacy of the suggested algorithms, this methodology may be used as a reliable method by medical professionals and policymakers for predicting Lung cancer.