A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…Based on the data spanning from 2005 to 2016 obtained from DMSP/SSUSI, we explore several machine learning algorithms, such as KNN, RF, and XGBoost, to construct an auroral oval prediction model. …”
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Predicting ICU mortality in heart failure patients based on blood tests and vital signs by Yeao Wang, Jianke Rong, Zhili Wei, Xiaoyu Bai, YunDan Deng

“…BackgroundCurrently, heart failure has become one of the major complications in the advanced stages of various cardiovascular diseases. Numerous predictive models have been developed to estimate the mortality rate of heart failure patients; however, these models often require the measurement of multiple indicators and the inclusion of various scoring systems. …”
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Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China by Haijun Wang, Xiangdong Kong, Onanong Phewnil, Ji Luo, Pengju Li, Xiyong Chen, Tianhui Xie

“…Using the WorldClim dataset as environmental variables, we predicted the future distribution of wetlands in western Sichuan under multiple climate scenarios. …”
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Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

“…Objectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients. …”
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Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation by Mi‐Na Kim, Yong Seok Lee, Youngmin Park, Ayoung Jung, Hanjee So, Joonwoong Park, Jin‐Joo Park, Dong‐Joo Choi, So‐Ree Kim, Seong‐Mi Park

“…In performing deep learning‐based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…The Extra Trees Classifier algorithm was found to be the best ML technique for predictions, according to the accuracy rates (92.3%) with an Area Under Curve of 0.92. …”
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VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels by Yuliana Jiménez-Gaona, Oscar Vivanco-Galván, Darwin Castillo-Malla, Israel Vivanco-Gualán, Patricia Díaz-Guzmán

“…Background: Vitamin D deficiency is a significant risk factor for several chronic conditions. This study aims to predict vitamin D deficiency levels in a private database, collected from the southern part of Loja-Ecuador using a graphical web interface tool based on artificial intelligence algorithms. …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…This study aimed to develop and evaluate predictive models for determining the need for RRT among patients with AP in the intensive care unit (ICU). …”
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Explainable machine learning model for predicting compressive strength of CO2-cured concrete by Jia Chu, Bingbing Guo, Taotao Zhong, Qinghao Guan, Yan Wang, Ditao Niu

“…Compared to conventional concrete, the factors to determine the compressive strength of CO2-cured concrete are more complex, and thus, predicting its compressive strength becomes more difficult. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…Results showcased high accuracy, with R2 values approaching 0.9912 in training and 0.9802 in testing phases using the LSA-XGB algorithm, indicating excellent model fit and predictive reliability. …”
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Cervical cancer prediction using machine learning models based on routine blood analysis by Jie Su, Hui Lu, Ruihuan Zhang, Na Cui, Chao Chen, Qin Si, Biao Song

“…This study aimed to develop an interpretable model for predicting CC risk using routine blood data. The primary endpoint variable is the occurrence of CC, as confirmed by histopathological diagnosis. …”
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Development and validation of a prediction model for VTE risk in gastric and esophageal cancer patients by Xingyue Zheng, Liuyun Wu, Lian Li, Yin Wang, Qinan Yin, Lizhu Han, Xingwei Wu, Yuan Bian

“…Using nine supervised learning algorithms, 576 prediction models were developed based on 56 available variables. …”
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A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis by Seyed Saeed Madani, Yasmin Shabeer, François Allard, Michael Fowler, Carlos Ziebert, Zuolu Wang, Satyam Panchal, Hicham Chaoui, Saad Mekhilef, Shi Xue Dou, Khay See, Kaveh Khalilpour

“…It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures.…”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

“…Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. …”
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Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…Background: An artificial intelligence (AI) approach can be used to predict venous thromboembolism (VTE). Objectives: To compare different AI models in predicting VTE using data from patients with COVID-19. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction by Shuangquan Zhang, Anjun Ma, Xuping Xie, Zhichao Lian, Yan Wang

“…In recent years, convolutional neural networks (CNNs) have succeeded in TF-DNA binding prediction, but existing DL methods’ accuracy needs to be improved and convolution function in TF-DNA binding prediction should be further explored. …”
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Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolit... by Sungwook Jung

“…However, although the number of confirmed cases is affected by social life, it is difficult to find studies that attempt to predict the number of confirmed cases using various lifestyle data. …”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…In summary, our approach involving data preprocessing, model tuning, feature selection, and explainability achieved state-of-the-art performance in predicting 30-day mortality rates following hip fractures surgery using a limited set of features, making it highly applicable in clinical settings.…”
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