Performance of five dynamic models in predicting tuberculosis incidence in three prisons in Thailand. by Nithinan Mahawan, Thanapoom Rattananupong, Puchong Sri-Uam, Wiroj Jiamjarasrangsi

“…This study examined the ability of the following five dynamic models for predicting pulmonary tuberculosis (PTB) incidence in a prison setting: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour and liters per second per person, the Issarow et al. model, and the applied susceptible-exposed-infected-recovered (SEIR) tuberculosis (TB) transmission model. …”
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Validation of user-friendly models predicting extracapsular extension in prostate cancer patients by Leandro Blas, Masaki Shiota, Shohei Nagakawa, Shigehiro Tsukahara, Takashi Matsumoto, Ken Lee, Keisuke Monji, Eiji Kashiwagi, Junichi Inokuchi, Masatoshi Eto

“…Objective: There are many models to predict extracapsular extension (ECE) in patients with prostate cancer. …”
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Predicting discharge coefficient of triangular side orifice using ANN and GEP models by Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed

“…This study utilized machine learning models to predict the discharge coefficient for a sharp-crested triangular side orifice (TSO). …”
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Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients by Dan Li, Han Lu, Junhui Wu, Hongbo Chen, Meidi Shen, Beibei Tong, Wen Zeng, Weixuan Wang, Shaomei Shang

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Evaluation of mathematical models for predicting medicine distribution into breastmilk - considering biological heterogeneity by Sumin Heo, Sumin Heo, Andrew S. Butler, Marina Stamouli Simoncioni, Sam Moult, Maria Malamatari, Essam Kerwash, Susan Cole

“…Subsequently, alternative methods for predicting lipid and protein binding within the milk, and the effect of ionisation and physicochemical properties were investigated.ResultsExisting models mis-predicted >40% of medications (Phase Distribution model), exhibited extreme sensitivity to milk pH (Log-Transformed model) or exhibited limited sensitivity to changes in creamatocrit (LogPo:w model). …”
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Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models by Zihan Li, Jiao Wang, Yixin Zhang, Zhen Yang, Fanchen Zhou, Xueting Bai, Qian Zhang, Wenchong Zhen, Rongxuan Xu, Wei Wu, Zhihan Yao, Xiaofeng Li, Yiming Yang

“…Survival outcomes were compared between different risk subgroups based on Kaplan-Meier analysis. Three predictive models were developed using machine learning(ML) techniques, and another was a nomogram based on COX proportional risk regression for estimating 3-year and 5-year overall survival in patients with epithelial ovarian cancer. …”
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Evaluation of machine learning models for predicting performance metrics of aero-engine combustors by Huan Yang, Shu Guo, Haolin Xie, Jian Wen, Jiarui Wang

“…The Extra Tree model exhibits superior predictive accuracy for various combustor performance metrics. …”
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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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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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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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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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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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COMPARISON OF LEAST SQUARE SPLINE AND ARIMA MODELS FOR PREDICTING INDONESIA COMPOSITE INDEX by Any Tsalasatul Fitriyah, Nur Chamidah, Toha Saifudin

“…The parametric approach in this study uses the ARIMA model. ARIMA is widely used to predict time series data. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes. …”
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Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable models by Ruoting Li, Sait Tunç, Osman Y. Özaltın, Matthew J. Ellis

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