Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach by Dong R, Wang Y, Yao H, Chen T, Zhou Q, Zhao B, Xu J

“…Cohorts 1 and 2 were used to create predictive models. The parameters of the machine learning model established by Cohorts 1 and 2 were merged, and nomogram models were developed using Logistic regression. …”
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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

“…Methods: In this retrospective analysis of 314 MG patients hospitalized between 2015 and 2018, we implemented four machine learning algorithms, including logistic regression, support vector machine, extreme gradient boosting (XGBoost), and random forest, to predict ICU admission risk. …”
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A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) by Muhammad Ricky Perdana Putra, Ema Utami

“…One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). …”
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Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model by Jiaqi Zhang, Jianing Zhang, Zhihao Liu, Yudong Zhou, Xiaoni Zhao, Yalong Wang, Danni Li, Jinsui Du, Chenglong Duan, Yi Pan, Qi Tian, Feiqian Wang, Ke Wang, Lizhe Zhu, Bin Wang

“…To optimize the utilization of biopsy, this study established a multimodal predictive framework that preoperatively assesses axillary lymph node (ALN) status, thereby triaging candidates for ultrasound-guided axillary biopsy. …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…Four machine learning algorithms were utilized, with the optimal model undergoing hyperparameter optimization to evaluate the significance of predictive factors.ResultsA total of 1,092 CKD patients were included, with 231 (21.2%) diagnosed with sarcopenia. …”
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Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization by Yuan Liu, Yuan Liu, Xin Yuan, Xin Yuan, Yu-Chan He, Yu-Chan He, Zhong-Hai Bi, Zhong-Hai Bi, Si-Yao Li, Si-Yao Li, Ye Li, Ye Li, Yan-Li Liu, Yan-Li Liu, Liu Miao, Liu Miao

“…PurposeTo investigate the predictive value of leukocyte subsets and C-reactive protein (CRP) in coronary artery disease (CAD).MethodsWe conducted a Mendelian randomization analysis (MR) on leukocyte subsets, C-reactive protein (CRP) and CAD, incorporating data from 68,624 patients who underwent coronary angiography from 2010 to 2022. …”
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A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018 by Wenqiang Wang, Ruiqing Mo, Xingyu Chen, Sijie Yang

“…Subsequently, nine machine learning algorithms—including logistic regression, random forest (RF), radial support vector machine (RSVM), k-nearest neighbors (KNN), XGBoost, LightGBM, decision tree (DT), elastic net regression (ENet), and multilayer perceptron (MLP)—were employed to construct predictive models. …”
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Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients by Jae Hun Chung, Jae Hun Chung, Jae Hun Chung, Yushin Kim, Dongjun Lee, Dongwon Lim, Dongwon Lim, Dongwon Lim, Sun-Hwi Hwang, Sun-Hwi Hwang, Sun-Hwi Hwang, Si-Hak Lee, Si-Hak Lee, Si-Hak Lee, Woohwan Jung

“…The confidence scores of the model indicated that the DSL predictions became more reliable over time.ConclusionThe study concluded that ML models, notably the XGB algorithm, can effectively predict DSL in real-time using comprehensive clinical data, enhancing the clinical decision-making process for GC patients.…”
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3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models by Hichem Horra, Ahmed Hadjadj, Elfakeur Abidi Saad, Khalil Moulay Brahim

“…A novel 3D rock strength prediction model that integrates geostatistic with deep learning algorithms is proposed. …”
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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches by Farveh Vahidpour, Hossein Sabouri, Fakhtak Taliei, Sayed Javad Sajadi, Saeed Yarahmadi, Hossein Hosseini Moghaddam

“…Abstract By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). …”
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Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome o... by Jakob Blaß, Benjamin Iffland, Philipp Herzog, Tim Kaiser, Thomas Elbert, Carolin Steuwe

“…A cross-validated genetic algorithm was used to detect baseline predictors of change in PTSD symptoms.Results: In the NET group higher education, more baseline PTSD symptoms, more traumatic experiences, fewer baseline BPD symptoms, and not taking antipsychotic medication predicted better treatment outcome. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…We assess the effectiveness of these algorithms by comparing their predictive accuracy with all available features and with a reduced set of selected features. …”
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Application of HHO-CNN-LSTM-based CMAQ correction model in air quality forecasting in Shanghai by ZHENG Xinnan, LIN Kaiyan, WANG Zijing, SONG Yuanbo, SHI Yang, LU Hanyue, ZHANG Yalei, SHEN Zheng*

Subjects: “…air quality prediction…”
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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

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A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion by Yaoxian Liu, Kaixin Zhang, Yue Sun, Jingwen Chen, Junshuo Chen

Subjects: “…photovoltaic output prediction…”
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EWT多重分解与若干新型元启发式算法优化的多层感知器月径流预测 by CAI Liang, BAO Yanfei, CUI Dongwen

Subjects: “…monthly runoff prediction…”
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Development of data driven models to accurately estimate density of fatty acid ethyl esters by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H. S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Abdolali Yarahmadi Kandahari

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Optimizing snow property forecasts over the tibetan plateau through hybrid assimilation of satellite precipitation and water vapor radiances using WRF model configured with Noah-MP by Jing Ren, Chunlin Huang, Jinliang Hou, Ying Zhang, Ling Yang

Subjects: “…Snow properties prediction…”
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Application of artificial neural network in determining the fabric weave pattern by Subrata Das, Keerthana Shanmugaraja

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Short-term traffic flow prediction research based on ICEEMDAN-MPE-PSO-DELM model by Xiujuan Tian, Jinyong Ding, Huanying Liu, Xue Xing, Jiaojiao Liu

“…In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. …”
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