Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. …”
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Retracted: Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Applied Bionics and Biomechanics

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Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine by Yun Deng, Lifan Xiao, Yuanyuan Shi

“…This study uses 206 hyperspectral soil samples from the state-owned Yachang and Huangmian Forest Farms in Guangxi, using the SPXY algorithm to partition the dataset in a 4:1 ratio, to provide an effective spectral data preprocessing method and a novel SOM content prediction model for the study area and similar regions. …”
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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
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Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm by Xiang Wu, Zhen Yang, Dongdong Wu

“…In recent years, the use of artificial intelligence algorithms for gas outburst prediction has made progress, such as using BP neural network, GA algorithm, and SVM algorithm. …”
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Performance Evaluation of a Radial Distribution Network Under Emerging Load Prediction Modeling Approach and DG Integration Using a Particle Swarm Optimization Algorithm by Demsew Mitiku Teferra

“…These performance metrics are evaluated under various load conditions, including base load and forecasted loads derived from both ANN and ANFIS predictions, incorporating DG integration. The results highlight that the PSO algorithm excels in optimizing network performance, achieving remarkable results across all evaluated parameters. …”
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Retracted: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm by Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha

“…Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12092)]. …”
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Correction: Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

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ProstaNet: A Novel Geometric Vector Perceptrons–Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation by Tianjian Liang, Ze-Yu Sun, Rieko Ishima, Xiang-Qun Xie, Ying Xue, Wei Li, Zhiwei Feng

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Diagnostic performance of a new algorithm combining simple, non-invasive and inexpensive tests for predicting the presence of advanced liver fibrosis in patients with chronic hepatitis B by Jean Nana, Jean Luc Bosson, Kristina Skaare, Céline Vermorel, Vincent Leroy, Tarik Asselah, Michael Adler, Jean-Pierre Zarski

“…Conclusion A new algorithm combining simple, non-invasive, and inexpensive tests demonstrates a good diagnostic value in predicting advanced liver fibrosis in patients with CHB or excluding significant fibrosis. …”
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Using a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer by Sheng-Yao Huang, Ren-Jun Hsu, Dai-Wei Liu, Wen-Lin Hsu

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Enhanced Wind Power Forecasting Using a Hybrid Multi-Strategy Coati Optimization Algorithm and Backpropagation Neural Network by Hua Yang, Zhan Shu, Zhonger Li

Subjects: “…wind power prediction…”
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Prediction model of water inrush risk level of coal seam floor based on KPCA-DBO-SVM by Wei Wang, Huangrui Wang, Xuping Li, Yun Qi, Xinchao Cui, Chenhao Bai

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Comprehensive flexible framework for using multi-machine learning methods to optimal dynamic transient stability prediction by considering prediction accuracy and time by Ali Abdalredha, Alireza Sobbouhi, Abolfazl Vahedi

“…In recent years, Machine/Deep Learning (ML/DL) techniques have been widely applied to predict transient stability conditions. This paper presents a flexible framework for using the desired number of ML algorithms and combines the results of them to extract the final optimal transient stability perdition (TSP). …”
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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according... by Lulu Liu, Qian Ma, Guangzan Yu, Xuhou Ji, Hua He

“…Moreover, we employed Boruta algorithm to evaluate the predictive potential of the NMa ratio and established the prediction models utilizing machine learning algorithms. …”
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Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models by Sughashini Murugesu, Kristofer Linton-Reid, Emily Braun, Jennifer Barcroft, Nina Cooper, Margaret Pikovsky, Alex Novak, Nina Parker, Catriona Stalder, Maya Al-Memar, Srdjan Saso, Eric O. Aboagye, Tom Bourne

“…Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. …”
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Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma by Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Fan Yang, Fan Yang, Fan Yang, Fan Yang, Wenjing Hou, Wenjing Hou, Wenjing Hou, Wenjing Hou, Ying Wang, Ying Wang, Ying Wang, Ying Wang, Jiali Mu, Jiali Mu, Jiali Mu, Jiali Mu, Hailing Wang, Hailing Wang, Hailing Wang, Hailing Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei

“…We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively.ResultsThe study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. …”
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Applications of Recent Metaheuristic Algorithms for Loss Reduction in Distribution Power Systems considering Maximum Penetration of Photovoltaic Units by Le Duy Luan Nguyen, Phuc Khai Nguyen, Viet Cuong Vo, Ngoc Dieu Vo, Thang Trung Nguyen, Tan Minh Phan

“…Photovoltaic units (PVUs) are placed optimally by implementing the Coot optimization algorithm (COOA), the archimedes optimization algorithm (AOA), the transient search optimization algorithm (TSOA), the crystal structure algorithm (CrSA), the war strategy optimization algorithm (WSA), and the average and subtraction-based optimizer (ASBO). …”
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Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning by Jiayuan Xu, Andrew J. Doig, Sofia Michopoulou, Sofia Michopoulou, Petroula Proitsi, Petroula Proitsi, Fumie Costen, The Alzheimer's disease neuroimaging initiative

“…This study aims to develop and validate machine learning algorithms for accurately predicting brain Aβ positivity using plasma biomarkers, genetic information, and clinical data as a cost-effective alternative to PET imaging.MethodsWe analyzed 1,043 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and validated our models on 127 patients from the Center for Neurodegeneration and Translational Neuroscience (CNTN) dataset. …”
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Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool by Jianying Liu, Wei Jiang, Yahong Yu, Jiali Gong, Guie Chen, Yuxing Yang, Chao Wang, Dalong Sun, Xuefeng Lu

“…In external validation, the SVM model maintained robust performance with an AUC of 0.889. The SHAP algorithm further explained the contribution of each feature to model predictions.Conclusion The study developed an interpretable and practical machine learning model for predicting bowel preparation adequacy in elderly patients, facilitating early interventions to improve outcomes and reduce resource wastage.…”
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