ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA by Gede Suwardika, I Ketut Putu Suniantara

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Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

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A type-theoretical approach to categorical interaction and complex system adaptation: an empirical study based on register classification by Renkui Hou, Chu-Ren Huang, Kathleen Ahrens

“…Linear regression is then applied to investigate how these ratios vary in different registers. …”
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Synergistic neighborhood relationships with travel behavior: An analysis of travel in 30,000 US neighborhoods by Carole Turley Voulgaris, Brian D. Taylor, Evelyn Blumenberg, Anne Brown, Kelcie Ralph

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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

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Risk stratification and clinical classification for postoperative neurological complications in post-tuberculosis kyphosis: a retrospective cohort study by Jianqiang Wang, Yong Hai, Haoshuang Geng, Zhangfu Li, Yuzeng Liu, Yangpu Zhang, Lijin Zhou

“…Multivariate logistic regression analysis revealed that the Baltalimani sign, spinal cord MRI type, Rajasekaran classification, three-column osteotomy, C-reactive protein (CRP), and SCA were significant risk factors for postoperative neurological complications. …”
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Cyberbullying-Related Automated Hate Speech Detection on Social Media Platforms Using Stack Ensemble Classification Method by Muhammad Mubeen, Aliza Muskan, Arslan Akram, Javed Rashid, Tagrid Abdullah N. Alshalali, Nadeem Sarwar

“…The research presents a stacked ensemble classification system that classifies tweets into three groups: hate speech, abusive language, or neutral. …”
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Preoperative clinical characteristics and risk assessment in Sun’s modified classification of Stanford type A acute aortic dissection by Jian Yao, Tao Bai, Chenyang Zhou, Bo Yang, Lizhong Sun

“…Binary logistic regression analysis showed that the Sun’s modified classification could not be used to assess the risk of preoperative rupture in TAAAD. …”
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A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study. by Xi Wang, Qiaoli Tao, Huanwen Liu, Huangbo Lin, Honglai Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify the optimal subsets of radiomics features, which were subsequently used to develop a support vector machine (SVM) classification model. …”
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A correlation analysis between serum cystatin C with Lee’s grading and Oxford classification of IgA nephropathy by CAI Xiao-fan, DENG Yue-yi, ZHONG Yi-fei, ZHENG Rong

“…According to Lee’s grading standard and interstitial fibrosis and tubuloatrophy( T) grading standard of Oxford classification,the patients were divided into 3 subgroups( mild,moderate and severe) separately and were also divided into 2 subgroups according to crescents( C) grading standard of Oxford classification. …”
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Identification of a coagulation-related classification and signature that predict disease heterogeneity for colorectal cancer and pan-cancer patients by Junpeng Pei, Junpeng Pei, Yuye Gao, Yuye Gao, Baojian Xing, Yongjiu Chen, Yongjiu Chen, Aiwen Wu, Aiwen Wu

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Non-Completely Displaced Traumatic Rib Fractures: Potentially Less Crucial for Pulmonary Adverse Outcomes, Regardless of Classification by Hongrye Kim, Su Young Yoon, Jonghee Han, Junepill Seok, Wu Seong Kang

“…<i>Background and Objectives</i>: Two major classification systems exist for rib fracture (RFX) displacement. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…The classifiers employed include Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Logistic Regression, which were further refined using GridSearchCV, RandomizedSearchCV, and Optuna, with 3-fold cross-validation implemented to ensure robust evaluation of model performance. …”
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Classification and correlation analysis of orbital medial wall fractures based on computed tomography data for prediction of surgical planning by Bei Li, Li Feng, Huamin Tang, Fang Wang, Wei Lin

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New Casemix Classification as an Alternative Method for Budget Allocation in Thai Oral Healthcare Service: A Pilot Study by Thunthita Wisaijohn, Atiphan Pimkhaokham, Phenkhae Lapying, Chumpot Itthichaisri, Supasit Pannarunothai, Isao Igarashi, Koichi Kawabuchi

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A New Classification System for Postinterventional Cerebral Hyperdensity: The Influence on Hemorrhagic Transformation and Clinical Prognosis in Acute Stroke by Yuan Shao, Yuyun Xu, Yumei Li, Xuehua Wen, Xiaodong He

“…Referring to the classification of HT, PCHD was classified as PCHD-1, PCHD-2, PCHD-3, and PCHD-4. …”
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A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

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Dysregulated interleukin networks drive immune heterogeneity in Alzheimer’s disease: an immunogenomic approach to subgroup classification and predictive modeling by Bin Zhang, Binglei Xu, Ruoxian Zhang, Baoying Gong, Jianwen Guo

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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

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