Causal Impact of Stock Price Crash Risk on Cost of Equity: Evidence from Chinese Markets by Babatounde Ifred Paterne Zonon, Xianzhi Wang, Chuang Chen, Mouhamed Bayane Bouraima

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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

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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

“…In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. …”
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Value of a BRAFV600E and lymphocyte subset-based nomogram for discriminating benign lesions from papillary thyroid carcinoma in C-TIRADS 3 and higher nodules by Wenran Zhang, Simei Zeng, Jiaqing Dou, Chenfan Yu

“…This study established and validated a nomogram model to quantitatively predict the malignant risk of papillary thyroid carcinoma in thyroid nodules classified as C-TIRADS category 3 or higher, providing a reference for precise diagnosis and treatment of these moderately or highly suspicious nodules.MethodsThis retrospective study analyzed 210 patients with thyroid nodules (C-TIRADS ≥3), stratified by fine-needle aspiration biopsy (FNAB) results into benign and PTC groups. …”
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Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals by Bangbei Tang, Bangbei Tang, Mingxin Zhu, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li

“…Six types of machine learning models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes) are trained and evaluated on this dataset.ResultsThe results demonstrate that all models can effectively classify driver olfactory preferences, and the decision tree model achieves the highest classification accuracy (88%) and F1-score (0.87). …”
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

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Modeling the Sustainable Land-Use Allocation in the Great Isfahan Using Multi-Criteria Evaluation in GIS Environment by Zeynab karimzadehMotlagh, Ali Lotfi, Saeid Pourmanafi

“…Moreover, three possible scenarios (current ecological and socio-economic trend, conservation of agricultural lands and urban-industrial development) can be designed and modeled based on. …”
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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

“…The proposed model outperformed traditional machine learning techniques as Random Forest, Support Vector Machine (SVM) and Decision Trees, demonstrating superior accuracy in tremor classification. …”
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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

“…Conclusions This study demonstrates that ML models using age, ADA, and LDH can effectively classify the etiologies of pleural effusion, suggesting that ML-based approaches may enhance diagnostic decision-making.…”
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CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study by Mengge He, Rahul Singh, Mandi Wang, Grace Ho, Esther M. F. Wong, Keith W. H. Chiu, Anthony K. T. Leung, Ka Yu Tse, Philip P. C. Ip, Andy Hwang, Lujun Han, Elaine Y. P. Lee

“…Results Seven hundred and three EOC patients (51.6 ± 9.3 years) were recruited. …”
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Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning by Wei Zou, Wei Zhong, Junzhen Du, Lingyun Yuan

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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…Moreover, we found that retaining lymph nodes with major axis <inline-formula> <tex-math notation="LaTeX">$\geq 8$ </tex-math></inline-formula>mm yields the best predictive results (C-index: 0.725), offering valuable guidance on choosing prognostic factors for esophageal cancer.For EC survival prediction using solely 3D CT images, integrating lymph node information with tumor information helps to improve the predictive performance of deep learning models.Clinical impact: The American Joint Committee on Cancer (TNM) classification serves as the primary framework for risk stratification, prognostic evaluation, and therapeutic decision-making in oncology. …”
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Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model by Wanling Li, Xin Zou, Jie Zhang, Minghong Hu, Guanfeng Chen, Shanshan Su

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A machine learning model for predicting fertilization following short‐term insemination using embryo images by Masato Saito, Hirofumi Haraguchi, Ikumi Nakajima, Shinya Fukuda, Chenghua Zhu, Norio Masuya, Kazunori Matsumoto, Yuya Yoshikawa, Tomoki Tanaka, Satoshi Kishigami, Leona Matsumoto

“…Abstract Purpose This study established a machine learning model (MLM) trained on embryo images to predict fertilization following short‐term insemination for early rescue ICSI and compared its predictive performance with the embryologist's manual classification. …”
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RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography by Yejia Gan, Wanzhong Huang, Yan Deng, Xiaoying Xie, Yuanyuan Gu, Yaozhuang Zhou, Qian Zhang, Maosheng Zhang, Yangchun Liu

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Development and Validation of a Prediction Model of the Risk of Pneumonia in Patients with SARS-CoV-2 Infection by Xi Yi, Daiyan Fu, Guiliang Wang, Lile Wang, Jirong Li

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Predictive modeling of multidrug resistance in female genital infections: implications for early urinary tract infection detection by Francis Chukwuebuka Ihenetu, Chinyere I. Okoro, Emeka Henry Okechukwu, Makuochukwu Maryann Ozoude, Farirai Melania Farirai

“…The predictive model showed moderate explanatory power (Nagelkerke R² = 0.233), good model fit (Hosmer-Lemeshow test, p = 0.961), and acceptable discriminatory ability (AUC = 0.753, p < 0.001), but had low sensitivity for MDR classification (2.8%). …”
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A novel hybrid extreme learning machine-based diagnosis model for sensor node faults in aquaculture by Bing Shi, Zelin Gao, Tianheng Pu, Jianming Jiang, Yueping Sun

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A novel data processing approach to detect fraudulent insurance claims for physical damage to cars by Ahmet Yücel

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