Impact of phthalate exposure and blood lipids on breast cancer risk: machine learning prediction by Yanbin Liu, Kunze Li, Yu Zhang, Yifan Cai, Xuanyu Liu, Yiwei Jia, Peizhuo Yao, Xinyu Wei, Huizi Wu, Xuan Liu, Cong Feng, Chaofan Li, Weiwei Wang, Shuqun Zhang, Chong Du

“…Notably, MIBP demonstrated the most significant predictive power in machine learning models. The predictive model’s accuracy, as indicated by the area under the ROC curve, was 87.1%. …”
Get full text

Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance by Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng

“…Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. …”
Get full text

An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
Get full text

Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV by Zhang W, Ren L, Yang K, Yan J, Yu Q, Qi S, Ruan H, Zhao D, Ruan L

Get full text

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis by Nooshin Tafazoli, Hooman Kamran, Roozbeh Bazargani, Mehrnoosh Samaei, Mitra Naseri, Abdol-Mohammad Kajbafzadeh

“…The machine learning modeling showed that for both febrile urinary tract infections and/or renal scarring and vesicoureteral reflux persistence, the random forest was the best fit. …”
Get full text

Establishment and validation of a dynamic nomogram to predict short-term prognosis and benefit of human immunoglobulin therapy in patients with novel bunyavirus sepsis in a populat... by Kai Yang, Bin Quan, Lingyan Xiao, Jianghua Yang, Dongyang Shi, Yongfu Liu, Jun Chen, Daguang Cui, Ying Zhang, Jianshe Xu, Qi Yuan, Yishan Zheng

“…Machine learning models, including Random Survival Forest, Stepwise Cox Modeling, and Lasso Cox Regression, were compared for their predictive performance. …”
Get full text

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–... by Bingtian Dong, Yuping Chen, Xiaocen Yang, Zhengdong Chen, Hua Zhang, Yuan Gao, Enfa Zhao, Chaoxue Zhang

“…Seven machine learning models were utilized to assess the predictive value of the eGDR index for CVD events. …”
Get full text

A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies by James Osborne, Chris Cockcroft, Carolyn Williams

“…Test case results were compared with pregnancy outcome data to assess performance.Results A machine-learning model was able to outperform current multivariate distribution models (McNemar’s p = .006, AUC 0.978 vs 0.974). …”
Get full text

Reproduction of reservoir pressure in oil field development: prospects and problems of using methods machine learning by I. N. Ponomarevа, D. A. Martyushev

Get full text

Use machine learning to predict bone metastasis of esophageal cancer: A population-based study by Jun Wan, Jia Zhou

“…Objective The objective of this study is to develop a machine learning (ML)-based predictive model for bone metastasis (BM) in esophageal cancer (EC) patients. …”
Get full text

The impact of direct and indirect digital soil mapping approaches on spatial uncertainty by Gábor Szatmári, László Pásztor

“…Such questions were examined on the example of mapping soil organic carbon (SOC) in the Great Hungarian Plain, Hungary, by combining machine learning with univariate and multivariate geostatistics. …”
Get full text

Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz by Md․Sohel Rana, Sheikh Md․ Rabiul Islam, Sanjukta Sarker

Get full text

Experimental Study of an Approximate Method for Calculating Entropy-Optimal Distributions in Randomized Machine Learning Problems by Alexey Yu. Popkov, Yuri A. Dubnov, Ilya V. Sochenkov, Yuri S. Popkov

“…Computational studies were carried out under the same conditions, with the same initial data and values of hyperparameters of the used models. They have shown the performance and efficiency of the proposed approach in the Randomized Machine Learning problems based on linear static models.…”
Get full text

Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning by Igor Džolev, Sofija Kekez-Baran, Andrija Rašeta

Get full text

Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study by Yaxuan Wang, Shiyang Xie, Jiayun Liu, He Wang, Jiangang Yu, Wenya Li, Aika Guan, Shun Xu, Yong Cui, Wenfei Tan

“…The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). …”
Get full text

A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach. by Michal Bozděch, Dominik Puda, Pavel Grasgruber

Get full text

Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

“…In this regard, the ensemble methods are highly effective for managing the multivariate nature of the task. Hard Voting aggregates multiple classifiers, becoming superior to individual models like support vector machines. …”
Get full text

Patterns of childhood trauma co-occurrence and its predictivity for suicidality: A machine learning approach by Wenbang Niu, Yi Feng, Shicun Xu, Xin Cui, Zhihao Ma, Yuanyuan Wang

Get full text

Ensemble-based customer churn prediction in banking: a voting classifier approach for improved client retention using demographic and behavioral data by Ruchika Bhuria, Sheifali Gupta, Upinder Kaur, Salil Bharany, Ateeq Ur Rehman, Seada Hussen, Ghanshyam G. Tejani, Pradeep Jangir

“…This work aims to categorize consumer turnover in banks by using a new ensemble approach combining many machine learning methods, hence enhancing churn prediction models. …”
Get full text

Post-TACE ALBI-Score Trajectory in Intermediate and Advanced Hepatocellular Carcinoma: Prognostic Implications and Influencing Factors Analysis by Li J, Feng T, Cui C, Wang H, Su T, Jin L, Zhao X, Xiao W

“…Monitoring these trajectories could guide personalized treatment strategies for HCC patients undergoing TACE.Keywords: hepatocellular carcinoma, transarterial chemoembolization, group-based trajectory modeling, machine learning, shapley additive explanations…”
Get full text