Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

Get full text

Influencing factors and prediction algorithms to estimate public preference for painless medical procedures in China: a national cross-sectional study by Xiangyang Guo, Xiaoxiao Wang, Yanan Song, Chen Luo, Zhengqian Li, Wei You, Sichen Liu, Kaixi Liu

Get full text

Fault Classification in Power Transformers via Dissolved Gas Analysis and Machine Learning Algorithms: A Systematic Literature Review by Vuyani M. N. Dladla, Bonginkosi A. Thango

“…In this paper, a systematic literature review (SLR) is conducted using the Preferred Reporting Items for Systematic Reviews (PRISMA) framework to record and screen current research work pertaining to the application of machine learning algorithms for DGA-based transformer fault classification. …”
Get full text

Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning by Wei Huang, Yue Xu, Zhao Li, Jun Li, Qing Chen, Qiang Huang, Yaping Wu, Hongtan Chen

“…Remarkably, for patients with mucinous cystic neoplasms (MCNs), regardless of undergoing MRI or CT imaging, the model achieved a 100% prediction accuracy rate. It indicates that our non-invasive multimodal machine learning model offers strong support for the early screening of MCNs, and represents a significant advancement in PCN diagnosis for improving clinical practice and patient outcomes. …”
Get full text

The implementation and appraisal of a novel confirmatory HIV-1 testing algorithm in the Microbicides Development Programme 301 Trial (MDP301). by Ute Jentsch, Precious Lunga, Charles Lacey, Jonathan Weber, Janet Cairns, Gisela Pinheiro, Sarah Joseph, Wendy Stevens, Sheena McCormack

“…This triggered the use of the algorithm which made use of archived serum and Buffy Coat samples. …”
Get full text

Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study by Tao Thi Tran, Jeonghee Lee, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim

Get full text

Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…Abstract Background Current breast cancer prediction models typically rely on personal information and medical history, with limited inclusion of blood-based biomarkers. …”
Get full text

Optimization method for educational resource recommendation combining LSTM and feature weighting by Meixia Yang

“…Ordinary educational resource recommendation models are usually based on simple search functions and user profiles for recommendation. …”
Get full text

Mean Nocturnal Baseline Impedance (MNBI) Provides Evidence for Standardized Management Algorithms of Nonacid Gastroesophageal Reflux-Induced Chronic Cough by Yiqing Zhu, Tongyangzi Zhang, Shengyuan Wang, Wanzhen Li, Wenbo Shi, Xiao Bai, Bingxian Sha, Mengru Zhang, Siwan Wen, Cuiqin Shi, Xianghuai Xu, Li Yu

“…Proximal MNBI < 2140 Ω may be used to screen patients with nonacid GERC suitable for standard antireflux therapy and in standardized management algorithms for nonacid GERC. …”
Get full text

Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort by Zhe Xu, Ruguo Zhang, Qiuhan Chen, Guoxuan Peng, Shanpeng Luo, Chen Liu, Ling Zeng, Jin Deng

“…The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms. …”
Get full text

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images by Fadwa Alrowais, Mohammed Alqahtani, Jahangir Khan, Achraf Ben Miled, Da’ad Albalawneh, Abdulwhab Alkharashi, Samah Al Zanin, Radwa Marzouk

Get full text

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study by Luis F Brenes, Luis A Trejo, Jose Antonio Cantoral-Ceballos, Daniela Aguilar-De León, Fresia Paloma Hernández-Moreno

Get full text

Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology by Yan Ye, Yuanyuan Chen, Jiajia Pan, Peipei Li, Feifei Ni, Haizhen He

“…Deep learning models hold the potential to enhance the accuracy of cervical cancer screening but require thorough evaluation to ascertain their practical utility. …”
Get full text

Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…The DT model showed comparable performance to RF (AUROC, 0.94; misclassification rate, 0.06). …”
Get full text

Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study by Shuhei Toba, Yoshihide Mitani, Yusuke Sugitani, Yusuke Sugitani, Hiroyuki Ohashi, Hirofumi Sawada, Mami Takeoka, Naoki Tsuboya, Kazunobu Ohya, Noriko Yodoya, Takato Yamasaki, Yuki Nakayama, Hisato Ito, Masahiro Hirayama, Motoshi Takao

“…For detecting electrocardiograms with ST-T abnormality, complete right bundle branch block, QRS axis abnormality, left ventricular hypertrophy, incomplete right bundle branch block, WPW syndrome, supraventricular tachyarrhythmia, and Brugada-type electrocardiograms, the specificity of the deep learning-based model was higher than that of the conventional algorithm at the same sensitivity.ConclusionsThe present new deep learning-based method of screening for abnormal electrocardiograms in children showed at least a similar diagnostic performance compared to that of a conventional algorithm. …”
Get full text

Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs by Jinjun Li, Kai Zhao, Guotai Yang, Haohao Lv, Renxin Zhang, Shuhan Li, Zhiyuan Chen, Min Xu, Naixue Yang, Shaoxing Dai

“…By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. …”
Get full text

microRNA-497-5p-based screening identifies a novel synthetic lethal-type interaction via PKMYT1 and WEE1 in pleural mesothelioma by Nathanael Pruett, Sierra Wilferd, Anand Singh, Agnes Y. Choi, Shivani Dixit, Vivek Singh, Charlize Nguyen, Olivia Lin, David S. Schrump, Christopher L. Plaisier, Chuong D. Hoang

Get full text

A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. by Abir Elbéji, Mégane Pizzimenti, Gloria Aguayo, Aurélie Fischer, Hanin Ayadi, Franck Mauvais-Jarvis, Jean-Pierre Riveline, Vladimir Despotovic, Guy Fagherazzi

“…The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. …”
Get full text

Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…First, the current compound search space for corrosion inhibitor molecule screening models remains limited. Second, these models face challenges related to computational resources and time costs in practical applications. …”
Get full text

A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors by Lisa Piazza, Miriana Di Stefano, Clarissa Poles, Giulia Bononi, Giulio Poli, Gioele Renzi, Salvatore Galati, Antonio Giordano, Marco Macchia, Fabrizio Carta, Claudiu T. Supuran, Tiziano Tuccinardi

“…The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. …”
Get full text