Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage by Weizhi Qiu, Penglei Cui, Shaojie Li, Zhenzhou Tang, Jiani Chen, Jiayin Wang, Yasong Li

“…Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. …”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. Data augmentation techniques address class imbalance, while SAEs facilitate accurate classification through layer-wise unsupervised pre-training and supervised fine-tuning. …”
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Human identification via digital palatal scans: a machine learning validation pilot study by Ákos Mikolicz, Botond Simon, Aida Roudgari, Arvin Shahbazi, János Vág

“…Abstract Background This study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set). …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

“…We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. …”
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Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer by Chongshuang Yang, Man Li, Xin Yi, Lin Wang, Guangxian Kuang, Chunfang Zhang, Benyong Yao, Zhihong Qin, Tianliang Shi, Qiang Jiang

“…All models showed good classification performance for PMI in both training and testing cohorts, with an AUC ranging from 0.755 to 1.000 in the training cohort and from 0.758 to 0.917 in the testing cohort. …”
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Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study by Xiaojiang Zhao, Yuhang Wang, Mengli Xue, Yun Ding, Han Zhang, Kai Wang, Jie Ren, Xin Li, Meilin Xu, Jun Lv, Zixiao Wang, Daqiang Sun

“…The performance of RAITS was then evaluated in both the train and validation cohorts. …”
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Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat by Yu Han, Jiaxue Zhang, Yan Bai, Zihao Liang, Xinhui Guo, Yu Zhao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Wude Yang, Guangxin Li, Sha Yang, Xingxing Qiao, Chao Wang

“…Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). …”
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Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model by Xiangguo Yang, Jia Tang, Qijia Song, Yifan Liu, Lin Liu, Xingwei Zhou, Yuelin Chen, Telu Tang

“…A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. …”
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Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study by Yanli Guo, Hongxia Zhang, Yang Guang, Wen He, Bin Ning, Chen Yin, Mingchang Zhao, Fang Nie, Pintong Huang, Rui-Fang Zhang, Qiang Yong, Jianjun Yuan, Yicheng Wang, Lijun Yuan, Litao Ruan, Tengfei Yu, Haiman Song, Yukang Zhang

“…To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.Results The AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).Conclusion DL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. …”
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Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study by Yi Xie, Yi Xie, Xinmeng Wang, Huiwen Yang, Huiwen Yang, Jiayao Zhang, Honglin Wang, Zineng Yan, Jiaming Yang, Zhiyuan Yan, Zhiwei Hao, Pengran Liu, Yijie Kuang, Zhewei Ye, Zhewei Ye

“…An explainable object detection algorithm was proposed for the identification of fractures. …”
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SAR remote sensing for monitoring harmful algal blooms using deep learning models by Kritnipit Phetanan, Do Hyuck Kwon, Jinmyeong Lee, Heewon Jeong, Gibeom Nam, Euiho Hwang, JongCheol Pyo, Kyung Hwa Cho

“…Evaluation metrics including precision, recall, and F1 scores yielded values of 0.600, 0.692, and 0.643, respectively. …”
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Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy by Nuo Yu, Yidong Wan, Lijing Zuo, Ying Cao, Dong Qu, Wenyang Liu, Lei Deng, Tao Zhang, Wenqing Wang, Jianyang Wang, Jima Lv, Zefen Xiao, Qinfu Feng, Zongmei Zhou, Nan Bi, Tianye Niu, Xin Wang

“…The predictive performance of the radiomics models was evaluated in the training cohort and verified in the validation cohort using AUC values. …”
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Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications by Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, Fèlix Llovell

“…Subsequently, data from the molecular characterization via polar soft-SAFT is used as output targets to train a machine learning algorithm based on artificial neural networks, enabling the prediction of mixture saturation properties out of the training dataset's scope. …”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…Future work will focus on training the model on larger, multi-source datasets to improve generalization across diverse populations. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…The best-performing models, DT and RF, were combined to create a stacking model demonstrating the most stable overall performance on training and test sets. The study identified key process parameters affecting PCE based on the stacking model. …”
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MP-SPILDL: A Massively Parallel Inductive Logic Learner in Description Logic by Eyad Algahtani

“…According to the experimental results using an Apache Spark implementation on a Hadoop cluster of three worker machines (36 total CPU cores, 7 total GPUs), MP-SPILDL achieved speedups of up to 13.3 folds using parallel beam search with <inline-formula> <tex-math notation="LaTeX">$beamWidth = 32$ </tex-math></inline-formula> and CPU-based vectorized hypothesis evaluation &#x2013; the best-case scenario. On small datasets such as Michalski&#x2019;s trains, MP-SPILDL achieved a slower performance than the baseline, representing the worst-case scenario.…”
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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. …”
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A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients by Meng Wang, Wei Li, Fang Zhou, Zheng Wang, Xiaoteng Jia, Xingpeng Han

“…Conclusion A novel NMRG signature was developed, contributing to the prognostic evaluation and personalized treatment for LUAD patients.…”
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Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

“…The aim of the study is to use machine learning techniques to develop predictive models that may be used to evaluate methylene blue dye degradation capacity in contaminated water. …”
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Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospectiv... by Nie F, Pei X, Du J, Shi W, Wang J, Feng L, Liu Y

“…The model’s predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). …”
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