Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal. by Susana Lavado, Eduardo Costa, Niclas F Sturm, Johannes S Tafferner, Octávio Rodrigues, Pedro Pita Barros, Leid Zejnilovic

“…This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. …”
Get full text

Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. by Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer, Michael Götz

“…Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.…”
Get full text

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

“…In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. …”
Get full text

Development of an Analytical Model for Determining the Magnetic Flux of Scattering through the Gears of the Stator of a Synchronous Electric Machine with a Fractional Gear Winding by A. V. Menzhinski, S. V. Panteleev, A. N. Malashin

“…The presented analytical model can be used in the process of optimizing a synchronous electric machine with fractional gear windings.…”
Get full text

Data‐driven designing on mechanical properties of biodegradable wrought zinc alloys by Zongqing Hu, Shaojie Li, Jianfeng Jin, Yuping Ren, Rui Hou, Lei Yang, Gaowu Qin

“…Finally, by combining multi‐objective genetic algorithm and RF models, the optimization alloy composition and extrusion parameters was carried out, targeting high‐strength, strength/plasticity synergy, and high plasticity for biodegradable purpose. …”
Get full text

A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…This study conducts a comparative performance analysis of the machine learning models for compressive strength prediction of FAGP using comprehensive dataset of 563 samples from 55 literature studies. …”
Get full text

Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV. by Zong-Bi Wu, You-Li Jiang, Shuai-Shuai Li, Ao Li

“…<h4>Methods</h4>Data for the machine learning model were obtained from the MIMIC-IV database. …”
Get full text

Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years. …”
Get full text

From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters by Chih-Fan Kuo, Yi-Chih Lin, Ze-Yu Chen, Jiunn-Horng Kang, Cheng-Chen Chang, Zhihe Chen, Arnab Majumdar, Yen-Ling Chen, Yi-Chun Kuan, Kang-Yun Lee, Po-Hao Feng, Kuan-Yuan Chen, Hsin-Chien Lee, Wun-Hao Cheng, Wen-Te Liu, Cheng-Yu Tsai

“…Therefore, this study developed machine learning models using leading indicators, such as heart rate variability (HRV) and oximetry-related metrics, to proactively predict optimal adjustment timings. …”
Get full text

Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices by Xie M, Liu Z, Dai F, Cao Z, Wang X

“…An interactive web tool was developed using the optimal model.Results: SAP incidence was 32.4%. LightGBM demonstrated superior predictive performance (ranking score=54) without overfitting, identifying Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), NIHSS score, age, aggregate index of systemic inflammation (AISI), and platelet-to-lymphocyte ratio (PLR) as the top predictors.Conclusion: Our findings demonstrate that machine learning models exhibit strong predictive performance for SAP, with the LightGBM algorithm outperforming other approaches. …”
Get full text

Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features by Xiaohua Yao, Mingming Tang, Min Lu, Jie Zhou, Debin Yang

“…We collected preoperative clinicopathological data and extracted, standardized radiomics features from elastography imaging to develop various ML models. These models were internally validated using radiomics and clinicopathological data, with the optimal model’s feature importance analyzed through the Shapley Additive Explanations (SHAP) approach and subsequently externally validated.ResultsIn our study of 485 patients, 67 (13.8%) exhibited SLNM. …”
Get full text

Modeling of concrete-filled PVC tube columns confined with CFRP strips under uniaxial eccentric compression: machine learning and finite element approaches by Yan Wang, Mohamed A. Elmeligy, Haytham F. Isleem, Asmaa Y. Hamed, Diyar N. Qader, Mohamed Sharaf, Pradeep Jangir, Arpita, Ghanshyam G. Tejani

“…Higher slenderness ratios decreased both capacities, particularly strain. Six machine learning models were employed to predict the load-carrying capacity and confined ultimate strain. …”
Get full text

Development of a machine learning-based predictive risk model combining fatty acid metabolism and ferroptosis for immunotherapy response and prognosis in prostate cancer by Zhenwei Wang, Zhihong Dai, Yuren Gao, Zhongxiang Zhao, Zhen Li, Liang Wang, Xiang Gao, Qiuqiu Qiu, Xiaofu Qiu, Zhiyu Liu

“…Using the TCGA-PRAD dataset, we identified 73 differentially expressed genes (DEGs) at the intersection of ferroptosis and FAM, of which 19 were significantly associated with progression-free survival (PFS). A machine learning-based prognostic model, optimized using the Lasso + Random Survival Forest (RSF) algorithm, achieved a high C-index of 0.876 and demonstrated strong predictive accuracy (1-, 2-, and 3-year AUCs: 0.77, 0.75, and 0.78, respectively). …”
Get full text

A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer by Shuang Zhang, Zhiyong Sun, Hasan Jafari

“…Furthermore, the model incorporates a newly developed metaheuristic algorithm called the Modified Electric Eel Foraging Optimization (MEEFO) algorithm to optimize the ELM parameters and enhance overall performance. …”
Get full text

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients. …”
Get full text

Machine Learning in the National Economy by Azamjon A. Usmonov

“…The main methods include an analysis of scientific literature, statistical data analysis, modeling using machine learning algorithms, and practical implementation of economic models with programming languages such as Python and machine learning libraries.To analyze economic data, methods such as linear regression, decision trees, and neural networks were selected, as they effectively predict changes in key macroeconomic indexes such as GDP, inflation, exchange rates, and unemployment levels. …”
Get full text

Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R² metrics. …”
Get full text

Anomaly detection of smart grid stealing network attacks based on deep autoencoder by Huang Yan, Li Jincan, Yang Xiaqin, Li Pei, Li Zi

Get full text

Trade-Space Exploration With Data Preprocessing and Machine Learning for Satellite Anomalies Reliability Classification by Abdul Mutholib, Nadirah Abdul Rahim, Teddy Surya Gunawan, Mira Kartiwi

“…This study introduces the Trade-Space Exploration Machine Learning (TSE-ML) framework, a comprehensive pipeline for satellite anomaly classification that optimizes preprocessing, transformation, normalization, and machine learning stages. …”
Get full text

Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning by Qiping Deng, Yu Xiong, Zirui Du, Jinping Cui, Cheng Peng, Zhiyong Luo, Jinli Xie, Hailong Qin, Zhimin Sun, Qingfeng Zeng, Kang Guan

“…Herein, we introduce a novel approach combining the cohesive finite element method (CFEM) with machine learning (ML) to address these challenges. …”
Get full text