Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

“…In addition, the prediction results of the XGBoost model were interpreted in detail using the SHAP algorithm. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…Additionally, transcription factors and potential therapeutic drugs were predicted. We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.…”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Results The hybrid model outperformed both baseline machine learning algorithms and ablated versions of itself. It achieved a ROCAUC of 0.9506 and an F1-score of 0.8333 on the SLE epitope prediction task. …”
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Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis by Qiqi Yan, Guiling Liu, Ruifeng Wang, Dandan Li, Xiaoli Chen, Deguang Wang

“…The Hosmer–Lemeshow test and calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting refractory peritonitis.Conclusion This nomogram can accurately predict refractory peritonitis in patients treated with PD.…”
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Improved breast cancer risk prediction using chromosomal-scale length variation by Yasaman Fatapour, James P. Brody

“…However, current tests based on SNPs do not perform much better than predictions based on family history and perform significantly worse in populations with non-European ancestry. …”
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Interpretable machine learning models for prolonged Emergency Department wait time prediction by Hao Wang, Nethra Sambamoorthi, Devin Sandlin, Usha Sambamoorthi

“…We employed five ML algorithms - cross-validation logistic regression (CVLR), random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), and support vector machine (SVM) - for predicting patient prolonged wait times. …”
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Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The aim of this study was to develop models based on Linear Discriminant Analysis (LDA), Classification and Regression Trees (C&RT), and Artificial Neural Network (ANN) for the prediction of the botanical origin of honeys using their physicochemical parameters as well as their antioxidative and thermal properties. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). …”
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Prediction models used in the progression of chronic kidney disease: A scoping review. by David K E Lim, James H Boyd, Elizabeth Thomas, Aron Chakera, Sawitchaya Tippaya, Ashley Irish, Justin Manuel, Kim Betts, Suzanne Robinson

“…<h4>Objective</h4>To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).…”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…Shapley additive explanation (SHAP)-based feature selection is typically applied to enhance interpretability, and its effect is compared with principal component analysis-based dimensionality reduction and nonselection methods. The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm by Wang Y, Ye S, Xu Z, Chu Y, Zhang J, Yu W

“…Yiwen Wang,1 Shuming Ye,2 Zhi Xu,3 Yonghua Chu,1 Jiarong Zhang,4 Wenke Yu5 1Clinical Medical Engineering Department, The Second Affiliated Hospital, Zhejiang University School of Medicine, HangZhou, ZheJiang, People’s Republic of China; 2Department of Biomedical Engineering, Zhejiang University, HangZhou, ZheJiang, People’s Republic of China; 3China Astronaut Research and Training Center, BeiJing, People’s Republic of China; 4Baidu Inc, BeiJing, People’s Republic of China; 5Radiology Department, ZheJiang Province Qing Chun Hospital, HangZhou, ZheJiang, People’s Republic of ChinaCorrespondence: Yiwen Wang; Shuming Ye, Email karenkaren2010@zju.edu.cn; ysmln@vip.sina.comPurpose: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.Methods: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Results show that machine learning algorithms including the Logistic Regression, Decision Trees, Random Forest, XGBoost, and Neural Networks schemes reliably predicted water potability in the absence of two missing instrumentation parameters namely: pH and DO. …”
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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with at least a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>160</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in the fault recall rate.…”
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Validating laboratory predictions of soil rewetting respiration pulses using field data by X. Li, X. Li, M. Pallandt, M. Pallandt, D. Naidu, D. Naidu, J. Rousk, G. Hugelius, G. Hugelius, S. Manzoni, S. Manzoni

“…Caution should be taken when extending laboratory insights for predicting fluxes in ecosystems.</p>…”
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An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang, Dajian Long, Yunxiu Li, Xiaozhu Liu, Zhi Bai, Zhongjun Li

“…Cervical Bishop score and interpregnancy interval showed the greatest impact on successful vaginal birth, according to SHAP results.Conclusions Models based on ML algorithms can be used to predict VBAC. The CatBoost model showed best performance in this study. …”
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A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Türkiye by Ulubey Çeken, Fadime Sertçelik, Abdullah İçen

“…In this study, we present new ground-motion prediction models (GMPMs) for shallow crustal earthquakes using strong-motion data recorded in Türkiye. …”
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A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids by Tomoyoshi Asano, Hidetaka Suga, Hirohiko Niioka, Hiroshi Yukawa, Mayu Sakakibara, Shiori Taga, Mika Soen, Tsutomu Miwata, Hiroo Sasaki, Tomomi Seki, Saki Hasegawa, Sou Murakami, Masatoshi Abe, Yoshinori Yasuda, Takashi Miyata, Tomoko Kobayashi, Mariko Sugiyama, Takeshi Onoue, Daisuke Hagiwara, Shintaro Iwama, Yoshinobu Baba, Hiroshi Arima

“…Furthermore, the model obtained by ensemble learning with the two algorithms can predict RAX expression in cells without RAX::VENUS, suggesting that our model can be deployed in clinical applications such as transplantation.…”
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