Predicting mortgage credit defaults in morocco using machine learning approaches by Amine Hade, Mohamed Elhia

“…Their performances were evaluated using metrics such as precision, recall, F1-score, and AUC. A bootstrap test, using the F1-score as the primary criterion, was conducted to determine whether the performance differences among the three top-performing models were statistically significant. …”
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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…In contrast, traditional machine learning and classical computer vision methods like Kernelized Correlation Filters (KCF), Tracking, Learning, and Detection (TLD), and Bootstrap Aggregating (BOOSTING), lacks reliability in performance.This paper introduces a machine learning-based approach to one-shot meta-learning for more efficient object tracking. …”
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Unraveling Phylogenetic Relationships via <em>A. carbonarius</em> and <em>A. tubingensis</em> Sequence Analyses by Sabah Ben Fredj Melki, Ehi Sheena Omoregie, Ahmed Mliki

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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<p>This study was carried out to determine the final weight estimation of Kıvırcık lambs using body measurements via Chi-square automatic interaction detection (CHAID), exhaustive CHAID, classification and regression tree (CART), random forest (RF), multivariate adaptive regression spline (MARS), and bootstrap-aggregating multivariate adaptive regression spline (Bagging MARS) algorithms. …”
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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…The contribution of each variable to the prediction was substantiated by creating a 95% confidence intervals of model coefficients by means of a 10,000 sample bootstrap and by analyzing top contributors across the best-performing models. …”
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Radiomics with Clinical Data and [¹⁸F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study by Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha, Ben R. Saleem

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Predictive Factors and Nomogram for Spontaneous Bacterial Peritonitis in Decompensated Cirrhosis Among the Elderly by Yan F, Peng X, Yang X, Yuan L, Zheng X, Yang Y

“…A multivariate logistic regression analysis was performed to identify significant predictors and to develop a nomogram for predicting the occurrence of SBP. To evaluate the model’s discrimination and calibration, a bootstrap method with 1000 resamples was utilized.Results: Findings from the multivariate logistic regression analysis indicated that constipation (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.25− 3.49, P=0.005), ascites (OR 2.84, 95% CI 1.64− 4.92, P< 0.001), Child-Pugh-Turcotte (CPT) score (OR 4.80, 95% CI 1.69− 13.60, P=0.003), and high sensitivity C-reactive protein (hs-CRP) (OR 2.96, 95% CI 1.54− 5.45, P=0.001) were significant independent predictors for the occurrence of SBP in elderly individuals with DC. …”
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Predictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery by Sun M, Chen WM, Lu Z, Lv S, Fu N, Yang Y, Wang Y, Miao M, Wu SY, Zhang J

“…Internal validation was executed using bootstrap sampling.Results: Among 111,069 patients, 1.6% developed chronic opioid dependence postoperatively. …”
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Simple Clinical Prediction Rules for Identifying Significant Liver Fibrosis: Evaluation of Established Scores and Development of the Aspartate Aminotransferase-Thrombocytopenia-Alb... by Puwitch Charoenchue, Jiraporn Khorana, Apichat Tantraworasin, Suwalee Pojchamarnwiputh, Wittanee Na Chiangmai, Amonlaya Amantakul, Taned Chitapanarux, Nakarin Inmutto

“…Internal validation confirmed model robustness, with an optimism-corrected AUROC of 0.8551. …”
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Development of a nomogram for predicting malignancy in BI-RADS 4 breast lesions using contrast-enhanced ultrasound and shear wave elastography parameters by Tiantian Ren, Zhenzhen Gao, Lufeng Yang, Weibo Cheng, Xiao Luo

“…The Young’s modulus was used for the SWE analysis. Bootstrap sampling was used to validate the nomogram. …”
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Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning by Peiyao Wang, Duo Zhou, Lingwei Hu, Pingping Ge, Ziyan Cen, Zhenzhen Hu, Qimin He, Kejun Zhou, Benqing Wu, Xinwen Huang

“…A non-invasive predictive model was developed, visualized as a nomogram, and internally validated using the enhanced bootstrap method. …”
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Seismic, Field, and Remote Sensing Analysis of the 13 February 2024 Çöpler Gold Mine Landslide, Erzincan, Türkiye by Pınar Büyükakpınar, Angela Cristina Carrillo-Ponce, Muhammad Badar Munir, Ezgi Karasözen, Hakan Tanyas, Deniz Ertuncay, Athul Palliath, Tolga Gorum

“…These pulses were analyzed using a single-force model with Bayesian bootstrap-based probabilistic inversion, providing insight into the complex forces driving the landslide. …”
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Evidence for YORP-induced Spin Deceleration in Asteroid (433) Eros by Shuai Feng, Shaoming Hu, Xu Chen, Liyong Zhou, Yangbo Xu, Zehua Qi

“…The discrepancy of υ values highlights the significant role of systematic errors in YORP detection. We calculated the theoretical YORP strength υ _mod using both 1D and 3D heat conduction models and found it smaller than the observed value υ , perhaps due to surface heterogeneity. …”
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Zebrafish krüppel-like factor 4a represses intestinal cell proliferation and promotes differentiation of intestinal cell lineages. by I-Chen Li, Chein-Tso Chan, Yu-Fen Lu, Yi-Ting Wu, Yi-Chung Chen, Guo-Bin Li, Che-Yi Lin, Sheng-Ping L Hwang

“…We used zebrafish as a model organism to gain further understanding of the role of Klf4 in the intestinal cell proliferation and differentiation.…”
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Predicting clinical outcome in posterior circulation large-vessel occlusion patients with endovascular recanalisation: the GNC score by Wei Li, Jing Qiu, Thanh Nguyen, Hui-Sheng Chen, Jia-Qi Wang, Si-Qi Qiu

“…This study sought to develop and validate a novel scoring system for predicting functional outcomes in pc-LVO cases following successful endovascular recanalisation.Methods We derived a predictive model from the DETECT-China cohort and externally validated it using the DETECT2-China dataset. …”
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Differences in bipolar disorder type I and type II exposed to childhood trauma: A retrospective cohort study by Hernán F Guillen-Burgos, Juan F Gálvez-Flórez, Sergio Moreno-Lopez, Angela T.H. Kwan, Oscar Gomez, Gerardo González-Haddad, Roger S. McIntyre

“…Univariate, bivariate analyses, and a Poisson regression model with bootstrap resampling were used. Results: Higher scores of CT, physical abuse (PA), and sexual abuse (SA) were statistically significant for BD II than BD I (p < 0.001, p = 0.048, p < 0.001, respectively). …”
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Longitudinal association between handgrip strength and depressive symptoms in middle-aged and older Chinese adults: mediating role of functional limitation by Yanchang Liu, Junling Cui, Xin Luo, Zhuzhu Wang, Ziyi Shen, Yan Fang, Chengcheng Li, Jingfang Hong

“…Functional limitation was evaluated based on participants’ self-reported basic activities of daily living (BADL) and instrumental activities of daily living (IADL). Logistic regression models were utilized to analyze the relationship between HGS and subsequent depressive symptoms, and bootstrap analysis was performed to explore the potential mediating role of functional limitation.ResultsAfter adjusting for confounders, an inverse correlation was detected between HGS and functional limitation (B = -0.885, p &lt; 0.001), a positive correlation was found between functional limitation and subsequent depressive symptoms (B = 1.054, p &lt; 0.001). …”
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Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms by Mohammad Reza Seif, Somaye Mohammadi, Parham Rahimi, Mehdi Behzad

“…RMS has emerged as the optimal characteristic for trend prediction, while Peak and Kurtosis have been identified as effective indicators for failure onset detection. The selection of the deterioration estimation model has involved comparing the outcomes of SVR + Bootstrapping and RVR models under various limited data scenarios. …”
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Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors by Catherine Park, Namhee Kim, Chang Won Won, Miji Kim

“…A machine learning approach incorporating recursive feature elimination and bootstrapping was employed to develop the prediction model. …”
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Phylogenetic analysis of mitochondrial D-loop sequence of Mongolian wild boars by Ali Khamit, Munkhjargal Bayarlkhagva, Davaa Bazarsad, Bolortuya Ulziibat, Bayarlkhagva Damdin, Bayarmaa Gun-Aajav

“…Sequence alignment, detection of parsimonious informative sites, model selection, calculation of nucleotide distances, and Maximum Likelihood (ML) phylogenetic tree construction with 1000 bootstrapped replications were conducted using MEGA X software. …”
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