Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts by Kaier Gu, Yang Liu

“…A comparison was made between the predictive capabilities of the ML models and clinical scores. Subsequently, the performance of the machine learning models was subjected to further validation within two external validation sets. …”
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Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models by Zhao Rong Liu, Zhao Rong Liu, Zhan Jiang Yu, Jie Zhou, Jian Biao Huang

“…For the top-performing prediction model, the study further employed the SHapley Additive exPlanations (SHAP) method to enhance model interpretability by elucidating the contribution of individual features to the prediction outcomes and ranking the relative importance of the predictive data. …”
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Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment by Yan Qin, Wei You, Yulong Wang, Yu Zhang, Zhijie Xu, Qingling Li, Yuelong Zhao, Zhiwei Mou, Yitao Mao

“…A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. <i>Results</i>: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. …”
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Establishment of Prediction Model of Axillary Lymph Node Metastasis Before Operation for Early-Stage Breast Cancer by Xinhua Zhang MM, Chuang Zhang MM, Jian Zhang MM, Xiuming Zhang MM, Xiaowen Dou MD

“…These variables were used to develop a predictive nomogram. Model performance was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA), assessing its accuracy, discrimination, calibration, and clinical utility. …”
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Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next? by Juan Diaz-Colunga, Ramon Diaz-Uriarte

“…Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. …”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…To further improve the performance of the model, SSA was increased to select relevant features. …”
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Recognition of Underwater Engineering Structures Using CNN Models and Data Expansion on Side-Scan Sonar Images by Xing Du, Yongfu Sun, Yupeng Song, Lifeng Dong, Changfei Tao, Dong Wang

“…The results demonstrate a test accuracy exceeding 92%, with data expansion improving small-sample model performance by over 7%. Notably, mutual influence effects were observed between categories, with similar features enhancing classification accuracy and distinct features causing inhibitory effects. …”
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Investigation of lump, breather and multi solitonic wave solutions to fractional nonlinear dynamical model with stability analysis by M.A. El-Shorbagy, Sonia Akram, Mati ur Rahman

“…Understanding MI is essential for developing systems that may either capitalize on its positive features or mitigate its adverse effects. Also, a comprehensive sensitivity analysis of the observed model is carried out to evaluate the influence of different factors. 3D surfaces and 2D visuals, contours, and density plots of the outcomes are represented with the help of a computer application. …”
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A Clinic-Radiomics Model for Predicting the Incidence of Persistent Organ Failure in Patients with Acute Necrotizing Pancreatitis by Nan Liu, Yidong Wan, Yifan Tong, Jie He, Shufeng Xu, Xi Hu, Chen Luo, Lei Xu, Feng Guo, Bo Shen, Hong Yu

“…Both primary and secondary endpoints were compared between the high- and low-risk groups stratified by the clinic-radiomics model. Results. According to the 547 selected radiomics features, four models were derived from features. …”
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Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN by Pinyan Li, Lap Man Hoi, Yapeng Wang, Xu Yang, Sio Kei Im

“…Currently, neural networks are widely employed for extracting speaker features. Although the Emphasized Channel Attention, Propagation, and Aggregation in Time Delay Neural Network (ECAPA-TDNN) model can obtain temporal context information through dilated convolution to some extent, this model falls short in acquiring fully comprehensive speech features. …”
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DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction by Aymin Javed, Nadeem Javaid, Nabil Alrajeh, Muhammad Aslam

“…Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. …”
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Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction by Yan Ding, Jinqi Zhu, Yang Liu, Dan Ning, Mingyue Qin

“…The proposed framework models battery degradation using state-space equations and extracts latent temporal features. …”
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Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma. by Caiyun Yao, Maotong Hu, Lingxia Zhou, Hui Chen, Yang Cao

“…The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained pathological images and to investigate the biological mechanisms linked to pathomics features by associating the pathomics model with transcriptomic data. …”
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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

“…Precise trajectory prediction is indispensable for augmenting vehicle safety and system performance in intricate environments. This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. …”
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A skin disease classification model based on multi scale combined efficient channel attention module by Hui Liu, Yibo Dou, Kai Wang, Yunmin Zou, Gan Sen, Xiangtao Liu, Huling Li

“…Firstly, the model has improved the pyramid segmentation attention module to extract multi-scale features of the image entirely. …”
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Enhanced Model for Gestational Diabetes Mellitus Prediction Using a Fusion Technique of Multiple Algorithms with Explainability by Ahmad Hassan, Saima Gulzar Ahmad, Tassawar Iqbal, Ehsan Ullah Munir, Kashif Ayyub, Naeem Ramzan

“…These results outperform the previous studies using the same GDM prediction dataset and demonstrate the model's superior performance. Additionally, Explainable Artificial Intelligence (XAI) techniques are utilized to interpret the model’s decisions. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Along with this is the use of a Fuzzy U-Net++ model for image segmentation, whereby fuzzy decisions in generously instill an increase in performance for image segmentation. …”
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Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms by Yonghan Luo, Wenrui Ding, Xiaotao Yang, Houxi Bai, Feng Jiao, Yan Guo, Ting Zhang, Xiu Zou, Yanchun Wang

“…Of the six predictive models developed, the XGBoost model demonstrated the highest performance in the training set (AUC = 0.926), though its performance in the test set was moderate (AUC = 0.740). …”
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An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification by Xin Liu, Qiao Meng, Xin Li, Zhijie Wang, Siyuan Kong, Bingyu Li

“…Initially, the model uses distance mapping between pixel features and frames of road traffic videos to construct a comprehensive road environment and vehicle segmentation model. …”
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Lightweight deep learning model with ResNet14 and spatial attention for anterior cruciate ligament diagnosis by Herman Herman, Yogan Jaya Kumar, Sek Yong Wee, Vinod Kumar Perhakaran

“…Data augmentation techniques were also implemented to mitigate biases. The model’s assessment uses performance metrics, ROC-AUC, sensitivity, and specificity. …”
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