Ordinal versus nominal regression models and the problem of correctly predicting draws in soccer by Hvattum L. M.

“…However, in practice, this flaw does not seem to have a substantial effect on the predictive accuracy of an ordered logit regression model when compared to a multinomial logistic regression model.…”
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Mortality predicting models for patients with infective endocarditis: a machine learning approach by Yang Zi-yang, Wang Qi, Xingyan Liu, Haolin Li, Shouhong Wang, Danqing Yu, Xuebiao Wei

Subjects: “…Model prediction…”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

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Two Machine-learning Hybrid Models for Predicting Type 2 Diabetes Mellitus by Rahman Farnoosh, Karlo Abnoosian, Rasha Abbas Isewid

“…Our proposed hybrid models demonstrated superior performance in two scenarios, handling and rejecting outliers, compared to other machine-learning models in this study, including support vector machines (with radial-based, polynomial, linear, and sigmoid kernel functions), decision trees (J48), and GNB classifiers for diabetes prediction. …”
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Predicting the Population Growth and Structure of China Based on Grey Fractional-Order Models by Xiaojun Guo, Rui Zhang, Naiming Xie, Jingliang Jin

“…In this paper, the fractional-order GM (1, 1) model and the fractional-order Verhulst model are established, respectively, based on the statistical data of China's population indices from 2015 to 2019 to forecast the population size and the change trend of population structure of China from 2015 to 2050 in the short-term and medium- to long-term. …”
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Regression models for predicting the effect of trash rack on flow properties at power intakes by Shuguang Li, Sultan Noman Qasem, Hojat Karami, Ely Salwana, Alireza Rezaei, Danyal Shahmirzadi, Shahab S. Band

“…Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. …”
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Different radiomics models in predicting the malignant potential of small intestinal stromal tumors by Yuxin Xie, Chongfeng Duan, Xuzhe Zhou, Xiaoming Zhou, Qiulin Shao, Xin Wang, Shuai Zhang, Fang Liu, Zhenbo Sun, Ruirui Zhao, Gang Wang

“…Objectives: To explore the feasibility of different radiomics models for predicting the malignant potential of small intestinal stromal tumors (SISTs), and to select the best radiomics model. …”
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Predicting binge drinking among university students: Application of integrated behavioral model. by Hordofa Gutema, Yamrot Debela, Bizuayehu Walle, Kidist Reba, Tebkew Shibabaw, Tolera Disasa

“…Linear and Logistic regression models were used to predict the role of explanatory variables on behavioral intention and binge drinking, respectively. …”
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Accuracy of Mathematical Models and Process Simulators for Predicting the Performance of Gas-Separation Membranes by Yousef Alqaheem

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Effectiveness of models of degradation of functional parameters for predicting the parametric reliability of semiconductor devices by S. M. Borovikov, N. I. Tsyrelchuk, S. S. Dick, E. N. Shneiderov

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Predicting Energy Consumption in Building Heating Systems Using Model Identification Methods by Qu Minglu, Du Shanghe, Zhang Xinlin, Yu Zhen, Li Huai

“…Using historical building data and simulation data of the heating system in TRNSYS, load prediction and equipment energy consumption models were established using the developed model identification method. …”
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Evaluation of Artificial Neural Network Model for Predicting Nitrogen Oxides (NOₓ) Concentration by Muhammad Farrih Mahabbataka Arsyada, Raras Tyasnurita

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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles by Wen-jie Huo, Fei Peng, Song Quan, Xiao-cong Wang

“…We then stratified predictions and actual yields into three categories (0, 1–2, and ≥ 3 blastocysts) to evaluate the model’s discriminative performance. …”
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A method for predicting postpartum depression via an ensemble neural network model by Yangyang Lin, Dongqin Zhou

“…In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction.…”
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iPRISM: Intelligent Predicting Response to Cancer Immunotherapy through Systematic Modeling by Yinchun Su, Siyuan Li, Qian Wang, Bingyue Pan, Jiyin Lai, Guangyou Wang, Junwei Han, Qingfei Kong

“…Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Herein, we present iPRISM (Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling), which is a novel network‐based model that integrates multiomics data to predict immunotherapy outcomes. …”
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Predicting global educational inequality with a hierarchical belief rule base model by Aosen Gong, Wei He, Gaixia Ge, Cuiping Yang, Shaohua Li

“…The Belief Rule Base (BRB) is used as an interpretable model that incorporates expert knowledge, making it suitable for these predictions. …”
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A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy by Min Li, Huifang Wang, Xiaoying Lai, Dandan Guo, Chunhui Jiang, Zixuan Fu, Xuemei Liu

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Employment of a Radial Basis Function Model for Predicting the Heating Load of Construction by Yuxuan Dai

“…The fact that this research is directed toward the goal of finding energy efficiency and cost-effectiveness, and generally toward the objective of improving the sustainability of the environment in building operation, speaks to the very central role that accurate HL prediction will play. These validations also prove that the RBPV model is the most outstanding regarding real-world applicability and accuracy. …”
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An intelligent model for predicting the behavior of soil conditions depending on external weather conditions by Antamoshkin Oleslav, Mikhalev Anton, Menshenin Andrey, Lukishin Alexander

“…This research integrates advanced machine learning models, including LSTM, Transformer, TCN, and XGBoost, to predict changes in road conditions based on meteorological and soil data. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. …”
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