Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer by Le Zhang, Chunqiu Zheng, Tian Li, Lei Xing, Han Zeng, Tingting Li, Huan Yang, Jia Cao, Badong Chen, Ziyuan Zhou

“…Our results demonstrate that (1) the explored genetic and environmental biomarkers are validated to connect to the CRC by biological function- or population-based evidences, (2) the model can efficiently predict the risk of CRC after parameter optimization by the big CRC-related data, and (3) our innovated heterogeneous ensemble learning model (HELM) and generalized kernel recursive maximum correntropy (GKRMC) algorithm have high prediction power. …”
Get full text

From spark to suppression: An overview of wildfire monitoring, progression prediction, and extinguishing techniques by Guangqing Zhai, Longhui Dou, Yifan Gu, Hongyi Zhou, Lele Feng, Liangliang Jiang, Jie Dong, Jiaxuan Sun, Haidong Li

“…Real-time wildfire risk prediction strategically guides fire force deployment, optimizing limited resources. …”
Get full text

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma by Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry

“…While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST‐based metrics. …”
Get full text

Liquid-liquid equilibrium data prediction using large margin nearest neighbor by mohsen pirdashti, kamyar movagharnejad, silvia Curteanu, Florin Leon, Farshad Rahimpour

“…To fill the theoretical gaps, the typical of support vector machines was applied to the k-nearest neighbor method in order to develop a regression model to predict the LLE equilibrium of guanidine hydrochloride in the above mentioned system. …”
Get full text

Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…To improve the efficiency of determining the optimal solid ink density, the Random Forest algorithm was applied for the first time to the prediction task of solid ink density in offset printing. …”
Get full text

Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…By introducing AI-driven systems into agricultural practices, this study aims to revolutionize disease identification, prediction, and management. The overarching objective is to minimize crop losses and enhance agricultural productivity. …”
Get full text

Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna by Md Mahabub Alam, Nurhafizah Abu Talip Yusof, Ahmad Afif Mohd Faudzi, Md Raihanul Islam Tomal, Md Ershadul Haque, Md. Suaibur Rahman

“…The interaction between the TE₁₁ cavity mode and the ring slots facilitates controlled electromagnetic field leakage, enhancing radiation performance. A predictive ML framework was developed using six regression algorithms trained on significant geometrical parameters, such as ring slot radius, via diameter, and feedline width. …”
Get full text

Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations by Anthony Shanks, Ben Steckler, Sarah Smith, Debra Rusk, Emily Walvoord, Erin Dafoe, Paul Wallach

“…We also sought to understand how the predicted scores affected student's plans. METHOD Traditional statistical models and machine learning algorithms to identify predictors of Step 2 CK performance were utilized. …”
Get full text

Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning by Chunxiu Zhao, Bingbing Xiang, Jie Zhang, Pingliang Yang, Qiaoli Liu, Shun Wang

“…Predictive models were constructed using nine different machine learning algorithms. …”
Get full text

Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

“…This paper aims to identify predictors and measure the performance of models used to predict the risk of IIT among People Living with HIV (PLHIV) on antiretroviral therapy (ART). …”
Get full text

Use of machine learning to predict creativity among nurses: a multidisciplinary approach by Rola H. Mudallal, Majd T. Mrayyan, Mohammad Kharabsheh

“…This study was aimed to explore the factors influencing nurses’ creativity and to develop a decision support system using machine learning to predict creativity levels among nurses. Methods A multidisciplinary design comprising machine learning algorithms mixed with a descriptive, cross-sectional, correlational design was implemented to enhance data analysis and decision-making. …”
Get full text

Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

“…In addition, the prediction results of the XGBoost model were interpreted in detail using the SHAP algorithm. …”
Get full text

Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
Get full text

Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…Additionally, transcription factors and potential therapeutic drugs were predicted. We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.…”
Get full text

A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Results The hybrid model outperformed both baseline machine learning algorithms and ablated versions of itself. It achieved a ROCAUC of 0.9506 and an F1-score of 0.8333 on the SLE epitope prediction task. …”
Get full text

Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis by Qiqi Yan, Guiling Liu, Ruifeng Wang, Dandan Li, Xiaoli Chen, Deguang Wang

“…The Hosmer–Lemeshow test and calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting refractory peritonitis.Conclusion This nomogram can accurately predict refractory peritonitis in patients treated with PD.…”
Get full text

Improved breast cancer risk prediction using chromosomal-scale length variation by Yasaman Fatapour, James P. Brody

“…However, current tests based on SNPs do not perform much better than predictions based on family history and perform significantly worse in populations with non-European ancestry. …”
Get full text

Interpretable machine learning models for prolonged Emergency Department wait time prediction by Hao Wang, Nethra Sambamoorthi, Devin Sandlin, Usha Sambamoorthi

“…We employed five ML algorithms - cross-validation logistic regression (CVLR), random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), and support vector machine (SVM) - for predicting patient prolonged wait times. …”
Get full text

Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The aim of this study was to develop models based on Linear Discriminant Analysis (LDA), Classification and Regression Trees (C&RT), and Artificial Neural Network (ANN) for the prediction of the botanical origin of honeys using their physicochemical parameters as well as their antioxidative and thermal properties. …”
Get full text

A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. …”
Get full text