Recently, many neural network methods have been proposed for multilabel classification in the literature. One of these recent methods is the Multi-Layer Extreme Learning Machines (ML-ELMs) in which stack auto encoders are used for tuning their weights. However, ML-ELMs suffer from three primary drawbacks: First, input weights and biases are chosen randomly; second, the pseudoinverse solution for calculating output weights will increase the reconstruction error; third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper, Multi-Layer Kernel Extreme Learning Machine (ML-CK-ELM) that uses a linear combination of base kernels in each layer is proposed for Multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, Multi-label classification data are inherently characterized by multi-modal aspects due to a variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of ML-CK-ELM that implicitly assesses the inherent multi-modal aspects of Multi-label data; each kernel can be effectively used to cover one of the modals better than other kernels. The empirical study indicates that ML-CK-ELM shows competitively better performance than other state-of-the-art methods, and experimental results of multilabel datasets verify the feasibility of ML-CK-ELM.

2Background and Aim: Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using machine learning. Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled. Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0. 01. Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases