An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease by Tanjila Alam Sathi, Rafsan Jany, Razia Zaman Ela, AKM Azad, Salem Ali Alyami, Md Azam Hossain, Iqram Hussain

“…This aggregated dataset was employed to train multiple machine learning (ML) models aimed at automatically classifying heart conditions, including arrhythmia, ischemia, and healthy states. We designed a predictive framework utilizing boosting ML algorithms, enhanced by explainable artificial intelligence (XAI) techniques, to ensure high predictive performance in model interpretation. …”
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Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review by Zeena Kailani, Lauren Kim, Joshua Bierbrier, Michael Balas, David J. Mathew, David J. Mathew

“…Artificial intelligence (AI) has emerged as a promising tool for enhancing predictive accuracy in clinical decision-making.MethodsThis systematic review was conducted to evaluate the current evidence on the use of AI to predict surgical outcomes in glaucoma patients. …”
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Machine learning for predicting medical outcomes associated with acute lithium poisoning by Omid Mehrpour, Varun Vohra, Samaneh Nakhaee, Seyed Ali Mohtarami, Farshad M. Shirazi

“…Abstract The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. …”
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Machine learning for predicting strength properties of waste iron slag concrete by Matiur Rahman Raju, Syed Ishtiaq Ahmad, Md Mehedi Hasan, Noor Md. Sadiqul Hasan, Md Monirul Islam, Md. Abdul Basit, Ishraq Tasnim Hossain, Saif Ahmed Santo, Md Shahrior Alam, Mahfuzur Rahman

“…The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. …”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…The model integrated multiple parameters, including the material composition (matrix material type and proportions), modification design (modifier type and content), optical properties (solar reflectance and infrared emissivity), and environmental factors (solar irradiance and ambient temperature) to achieve accurate cooling performance predictions. A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R² value of 0.943 and an RMSE of 1.423 for the test dataset. …”
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Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy by Xu Yang, Qi Long, Hao Li, Dachao Huang, Shupeng Xue, Jiajie Huang, Hongzhang Liang, Xiongying Duan

“…To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. …”
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Different artificial neural networks for predicting burnout risk in Italian anesthesiologists by Marco Cascella, Alessandro Simonini, Sergio Coluccia, Elena Giovanna Bignami, Gilberto Fiore, Emiliano Petrucci, Alessandro Vergallo, Giacomo Sollecchia, Franco Marinangeli, Roberto Pedone, Alessandro Vittori

“…Despite substantial differences among the six implemented algorithms, no significant variation in prediction performance was observed. …”
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Using machine learning to predict the rupture risk of multiple intracranial aneurysms by Junqiang Feng, Chunyi Wang, Yu Wang, He Liu, He Liu

“…Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.…”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. …”
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Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome by Ge Jin, Xiaomei Fan, Xiaoliang Liang, Honghong Dai, Jun Wang

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”
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Methodic approaches to prediction of a positive corporate image of pharmaceutical organizations by A. G. Petrov, O. I. Knysh, G. P. Petrov

“…The technique includes the algorithm for construction of estimation and prediction tables and rules of their usage.…”
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The Influence of Non-Landslide Sample Selection Methods on Landslide Susceptibility Prediction by Yu Fu, Zhihao Fan, Xiangzhi Li, Pengyu Wang, Xiaoyue Sun, Yu Ren, Wengeng Cao

“…Additionally, the EIV method identified smaller, more concentrated high-susceptibility zones, covering 87.37% of historical landslide points, compared to the larger, less precise zones predicted by other methods. This study highlights the effectiveness of the EIV method in refining non-landslide sample selection and improving landslide susceptibility prediction, providing valuable insights for disaster risk reduction and land use planning.…”
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Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models by Wenqing Li, Jing Lv, Yuhang Wang, Xiangfeng Kong

“…The intelligent parameter optimization framework proposed in this study provides theoretical support for the development of a marine ranching DO monitoring system, and its technical path can be extended to the prediction of other water environment indicators. Future research will develop a parameter adaptive selection algorithm, conduct the dynamic monitoring of multi-scale environmental factors, and achieve the intelligent optimization and verification of model parameters.…”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…It outlines the evolution of computational chemistry and the transformative role of AI in interpreting complex molecular data, automating feature extraction, and improving decision-making across the drug development pipeline. Core AI algorithms support vector machines, random forests, graph neural networks, and transformers are examined for their applications in molecular representation, virtual screening, and ADMET property prediction. …”
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Predicting 24-hour intraocular pressure peaks and averages with machine learning by Ranran Chen, Jinming Lei, Yujie Liao, Yiping Jin, Xue Wang, Xiaomei Li, Danping Wu, Hong Li, Yanlong Bi, Haohao Zhu

“…Predictive models based on five machine learning algorithms were trained and evaluated. …”
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Using machine learning models to predict post-revascularization thrombosis in PAD by Samir Ghandour, Adriana A. Rodriguez Alvarez, Isabella F. Cieri, Shiv Patel, Mounika Boya, Rahul Chaudhary, Rahul Chaudhary, Rahul Chaudhary, Anna Poucey, Anahita Dua

“…BackgroundGraft/ stent thrombosis after lower extremity revascularization (LER) is a serious complication in patients with peripheral arterial disease (PAD), often leading to amputation. Thus, predicting arterial thrombotic events (ATE) within 1 year is crucial. …”
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Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks by Emmanuel Oluwatosin Rabiu, Damilare Oluwole Akande, Zachaeus Kayode Adeyemo, Isaac Akinwale Akanbi, Oluwole Oladele Obanisola

“…However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. …”
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