Interpretable graph Kolmogorov–Arnold networks for multi-cancer classification and biomarker identification using multi-omics data by Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Harshith Guduru, Mohanad Mohammed

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Development of a severity score based on the International Classification of Disease-10 for general patients visiting emergency centers by Ji Eun Kim, Jinwoo Jeong, Yuri Choi, Sung Woo Lee

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Comparative Analysis of Machine Learning and Deep Learning Models for Classification and Prediction of Liver Disease in Patients with Hepatitis C by Shalem Preetham Gandu, M. Roshni Thanka, E. Bijolin Edwin, Ebenezer Veemaraj, S. Stewart Kirubakaran

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A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images by Oguzhan Katar, Ozal Yildirim, Ru-San Tan, U Rajendra Acharya

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Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine by Hengki Muradi, Dede Dirgahayu Domiri, I Made Parsa, I Kadek Yoga, Alhadi Bustamam, Anisa Rarasati, Sri Harini, R. Johannes Manalu, Mokhamad Subehi

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Pain classification using functional near infrared spectroscopy and assessment of virtual reality effects in cancer pain management by Somayeh B. Shafiei, Saeed Shadpour, Barbara Pangburn, Martha Bentley-McLachlan, Oscar de Leon-Casasola

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Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms by M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami

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Correction: Application of the comprehensive complication index and Clavien–Dindo complication classification in cardiac surgery: a retrospective study by Zhen-Rong Zhang, Yang-Zheng Li, Xiao-Qing Wu, Wen-Jun Chen, Jie-Yang Yuan, Min Yan

“…Results 563 patients (76.3%) had at least one complication. The mean CCI was 22.5 ± 19.3. …”
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Machine learning classification algorithm screening for the main controlling factors of heavy oil CO2 huff and puff by Peng-xiang Diwu, Beichen Zhao, Hangxiangpan Wang, Chao Wen, Siwei Nie, Wenjing Wei, A-qiao Li, Jingjie Xu, Fengyuan Zhang

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Tanshinone Content Prediction and Geographical Origin Classification of <i>Salvia miltiorrhiza</i> by Combining Hyperspectral Imaging with Chemometrics by Yaoyao Dai, Binbin Yan, Feng Xiong, Ruibin Bai, Siman Wang, Lanping Guo, Jian Yang

“…The results indicated that the D1-PLS-DA model performed the best with a classification accuracy of 98.97%. SG-BPNN achieved the best prediction effect for cryptotanshinone (RMSEP = 0.527, RPD = 3.25), ORI-BPNN achieved the best prediction effect for tanshinone IIA (RMSEP = 0.332, RPD = 3.34), MSC-PLSR achieved the best prediction effect for tanshinone I (RMSEP = 0.110, RPD = 4.03), and SNV-BPNN achieved the best prediction effect for total tanshinones (RMSEP = 0.759, RPD = 4.01). …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

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Comparative analysis of prognostic assessment in hospitalized heart failure patients: a comprehensive evaluation of KDIGO and WRF classifications by Chien-Hao Su, Chien-Hao Su, Pei-Chun Fan, Pei-Chun Fan, Ya-Lien Cheng, Pao-Chu Wu, Chao-Yu Chen, Cheng-Chia Lee, Cheng-Chia Lee, Yung-Chang Chen, Victor Chien-Chia Wu, Pao-Hsien Chu, Chih-Hsiang Chang, Chih-Hsiang Chang

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Validation and optimization of classification criteria for childhood-onset systemic lupus erythematosus in a multi-center Chinese cohort by Yudi Zhang, Tian Shen, Sihao Gao, Junxia Yan, Jingcheng Shi, Xuemei Tang, Mo Wang, Jun Yang, Haiguo Yu, Huawei Mao, Lanjun Shuai, Yongzhen Li, Yan Cao, Xiaoyan Li, Ying Wang, Qian Liu, Hongmei Song, Xiaochuan Wu

“…Multiple optimization schemes were then developed through LASSO regression with expert consultation in the derivation cohort. …”
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Impact of parathyroid gland classification on hypoparathyroidism following total thyroidectomy with central neck dissection for differentiated thyroid cancer by Qixuan Sheng, Wei Li, Ping Zhang, Qiang Wang, Siluo Zha, Wensheng Rao, Bin Wang, Xinyun Xu, Ming Qiu, Wei Zhang, Chengxiang Shan

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Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES Strin... by Maciej Przybyłek, Tomasz Jeliński, Piotr Cysewski

“…A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. …”
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LogiTide2DEM: A method for reconstructing intertidal topography in complex tidal flats using logistic regression with multi-temporal Sentinel-2 and Landsat imagery by Yi-Chin Chen, Su-Fen Wang

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Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality by Hildegardis Kristina saka, Putri Taqwa Prasetyaningrum

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Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features by Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

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A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy by Jiaqi Wang, Kangjing Xu, Changsheng Zhou, Xinbo Wang, Junbo Zuo, Chenghao Zeng, Pinwen Zhou, Xuejin Gao, Li Zhang, Xinying Wang

“…Visceral adipose volume (VAV) and subcutaneous adipose volume (SAV) were measured using three-dimensional (3D) computed tomography (CT) reconstruction. …”
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Molecular classification of head and neck squamous cell carcinoma based on hypoxia-related genes and clinical significance of STC2 by ZHU Jianing, WANG Tiantian, ZHANG Rui, SONG Hongquan

“…Results Based on HRGs expression profiles, HNSCC was categorized into three subclasses (C1, C2, and C3). Subclass C1 had moderate hypoxic activity and good prognosis; subclass C2 had the highest hypoxic activity, poor prognosis, and poor sensitivity to CTLA-4 inhibitors (<i>P</i>&lt;0.05); subclass C3 had the lowest hypoxic activity and moderate prognosis, and STC2 belonged to subclass C3. …”
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