Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications by Shiquan Liu, Hao Sun, Tianye Song, Ce Liang, Lele Deng, Haiyong Zhu, Fangchao Zhao, Shujun Li

“…A Lasso + PLSRcox-based signature was a significant risk factor for predicting LUAD patient outcomes, outperforming traditional clinicopathological factors. …”
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An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy by Nalini Manogaran, Nirupama Panabakam, Durai Selvaraj, Koteeswaran Seerangan, Firoz Khan, Shitharth Selvarajan

Subjects: “…Patient’s response prediction…”
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Assessing sepsis-induced immunosuppression to predict positive blood cultures by Enrique Hernández-Jiménez, Enrique Hernández-Jiménez, Erika P. Plata-Menchaca, Erika P. Plata-Menchaca, Damaris Berbel, Damaris Berbel, Guillem López de Egea, Guillem López de Egea, Macarena Dastis-Arias, Laura García-Tejada, Fabrizio Sbraga, Pierre Malchair, Nadia García Muñoz, Alejandra Larrad Blasco, Eva Molina Ramírez, Xose Pérez Fernández, Joan Sabater Riera, Arnau Ulsamer

“…Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. …”
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Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence by Aliakbar Sayyari, Amin Magsudy, Yasamin Moeinipour, Amirhossein Hosseini, Hamidreza Amiri, Mohammadreza Arzaghi, Fereshteh Sohrabivafa, Seyedeh Fatemeh Hamzavi, Ashkan Azizi, Tahereh Hatamii, AmirAli Okhovat, Naghi Dara, Negar Imanzadeh, Farid Imanzadeh, Mahmoud Hajipour

“…Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis.ObjectiveThis study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis.MethodsData from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023, were analyzed. …”
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Comparison Of Reversible Image Watermarking Methods Based On Prediction-Errors by Burhan Baraklı, Emre Altınkaya

“…This study compares two reversible imagewatermarking algorithms applied to a digital image. The first algorithm is amethod based on adaptive watermarking of prediction-errors. …”
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Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees by Joaquin Alvarez, Edgar Roman-Rangel

“…In this work, we introduce a framework to combine arbitrary image segmentation algorithms from different agents under data privacy constraints to produce an aggregated prediction set satisfying finite-sample risk control guarantees. …”
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Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

“…Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). …”
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Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis by Anjing Wang, Anjing Wang, Yunlong Qin, Yunlong Qin, Yan Xing, Zixian Yu, Liuyifei Huang, Jinguo Yuan, Yueqing Hui, Mei Han, Guoshuang Xu, Jin Zhao, Shiren Sun

“…The RSF model we established for class IV ± V LN patients, incorporating seven risk factors, exhibits superior survival prediction and provides more precise prognostic stratification.…”
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Assessment of methods for predicting physical and chemical properties of organic compounds by Tunga Salthammer

“…However, with the increasing performance of computers, prediction tools based on structure-activity relationships and quantum mechanical calculations have become increasingly popular. …”
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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. …”
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A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases by Diego Mariano

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TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction by I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner

“…Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. …”
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Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. …”
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The RMaP challenge of predicting RNA modifications by nanopore sequencing by Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek P. Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Kei Wan, Jonathan Göke, Andreas Hildebrandt, Christoph Dieterich, Mark Helm, Manja Marz, Susanne Gerber, Nicolo Alagna

“…Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. …”
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AI based predictive acceptability model for effective vaccine delivery in healthcare systems by Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi, Urooj Iqrar, Ali Raza, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Ayman Qahmash

“…A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. …”
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Toward Intelligent Fading Channel Prediction: A Comprehensive Survey by Ramoni Adeogun

“…Through this survey, we aim to provide a foundation for future research in intelligent channel prediction, highlighting the need for more sophisticated and adaptive algorithms to cope with the increasing complexity of wireless communication systems.…”
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Research on Customer Churn Prediction Using Machine Learning Models by Jia Xiaolei

“…With the increasing availability of customer data and advancements in machine learning techniques, accurate churn prediction has become more feasible and impactful. This research compares and analyzes the advantages and disadvantages of three different machine learning algorithms applied to customer churn prediction: random forest, decision tree, and neural network. …”
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Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model by Jonas Turnwald, David Valero, Pedro Forte, Katja Weiss, Elias Villiger, Mabliny Thuany, Volker Scheer, Matthias Wilhelm, Marilia Andrade, Ivan Cuk, Pantelis T. Nikolaidis, Beat Knechtle

“…Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. …”
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Use of Machine Learning to Predict California Bearing Ratio of Soils by Semachew Molla Kassa, Betelhem Zewdu Wubineh

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