Search Results - best first three algorithm

Ultra-long Distance Distributed Intrusion Detecting System Assisted With In-line Amplification by Macheng Lai, Kuan Peng, Yiyang Luo, Xiaolei Li, Yanpeng Li, Fan Ai, Deming Liu, Qizhen Sun

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A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study by Yanan Liu, Lei Fan, Wencai Wang, Hongxuan Song, Zhenhua Zhang, Qian Liu, Zhongji Meng, Shibo Li, Hua Wang, Shijun Zhou, Wanjun Liu, Guomei Xia, Jianping Duan, Chunxia Guo, Lu Wang, Ling Xu, Tong Wang, Hanxin Li, Xinyue Zhang, Tiandan Xiang, Di Liu, Zujiang Yu, Yuliang Liu, Junzhong Wang, Xin Zheng

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Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning by Yao F, Miao J, Quan B, Li J, Tang B, Lu S, Yin X

“…Fan Yao,1,2,* Jianliang Miao,3,* Bing Quan,1,2 Jinghuan Li,1,2 Bei Tang,1,2 Shenxin Lu,1,2 Xin Yin1,2 1Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 2National Clinical Research Center for Interventional Medicine, Shanghai, People’s Republic of China; 3First Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xin Yin, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai, People’s Republic of China, Email yin.xin@zs-hospital.sh.cnPurpose: To establish prediction models using Shapley Additive exPlanations (SHAP) and multiple machine learning (ML) algorithms to identify clinical features influencing hepatic arterial infusion chemotherapy (HAIC) resistance and survival in patients with hepatocellular carcinoma (HCC).Patients and Methods: We recruited 286 patients with unresectable HCC who underwent HAIC. …”
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Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome by M. M. Tsivanyuk, B. I. Geltser, K. I. Shakhgeldyan, E. D. Emtseva, G. S. Zavalin, O. I. Shekunova

“…The quality metrics of the best predictive algorithm based on MLR were 0,81 for AUC, 0,74 for Sp and Ac, and 0,75 for Se. …”
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Soil Carbon Remote Sensing: A Meta-Analysis and Systematic Review of Published Results from 1969–2022 by Savannah L. McGuirk, Iver H. Cairns

“…Fourth, the R² of non-parametric models (mean R² in 2022 = 0.58, n = 117) has declined more rapidly (decrease of 1.3% per year) since 1969 (mean R² in 1969 = 0.74, n = 1) than the R² of parametric models (decrease of 0.4% per year), suggesting that the algorithm applied during soil carbon modelling may be of importance. …”
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Structure–property models of organic compounds based on molecular graphs with elements of the spatial structures of the molecules by N. A. Shulaeva, M. I. Skvortsova, N. A. Mikhailova

“…This article aims to describe, elaborate, and test a general algorithmic method for constructing the structure–property models for organic compounds.Results. …”
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Features of Femtosecond Correction of High Myopia through Small Access by O. V. Pisarevskaya, A. G. Shchuko, T. N. Iureva, E. M. Balgirova, T. N. Frolova, L. S. Khlebnikova

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Parameters of complete blood count, lipid profile and their ratios in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome by M. M. Tsivanyuk, B. I. Geltser, K. I. Shakhgeldyan, A. A. Vishnevskiy, O. I. Shekunova

“…Two groups were formed, the first of which consisted of 360 (60%) patients with oCAD (stenosis ≥50%), and the second — 240 (40%) with coronary stenosis <50%. …”
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Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve by Wentao Li, Yunqin He, Yiyi Zhang, Guozhu Liang

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Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology by Qihang Xie, Haoran Qu, Jianfeng Li, Rui Zeng, Wenhao Li, Rui Ouyang, Chengxiang Zhang, Siyu Xie, Siyu Xie, Ming Du

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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Patients were randomly assigned to training (n = 196) and validation (n = 83) cohorts in a 7:3 ratio. Feature selection was performed using univariate Cox regression (p ≤ 0.05), and four ML algorithms—Random Survival Forest (RSF), Gradient Boosting Machine (GBM), CoxBoost, and XGBoost—were applied to develop recurrence prediction models. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…The final pH was found to become acidic after treatment. First-order rate kinetics best fit the degradation kinetic data, and the UV irradiation, PO, and PS contributed 9.9 %, 15.3 %, and 75.7 % of the total removal of the antibiotics. …”
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Explainable machine learning for predicting distant metastases in renal cell carcinoma patients: a population-based retrospective study by Zhao Hou, Zhao Hou, Peipei Wang, Peipei Wang, Dingyang Lv, Dingyang Lv, Huiyu Zhou, Huiyu Zhou, Zhiwei Guo, Zhiwei Guo, Jinshuai Li, Jinshuai Li, Mohan Jia, Mohan Jia, Hongyang Du, Hongyang Du, Weibing Shuang, Weibing Shuang

“…Shapley additive explanations (SHAP) were used for model interpretation. The best-performing model was then used to create a web-based calculator to predict metastasis risk in RCC patients.ResultsThe study included 51,566 RCC patients, with 3,667 showing distant metastases. …”
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Correction of ASCAT, ESA–CCI, and SMAP Soil Moisture Products Using the Multi-Source Long Short-Term Memory (MLSTM) by Qiuxia Xie, Yonghui Chen, Qiting Chen, Chunmei Wang, Yelin Huang

“…The results showed that the <i>RMSE</i> values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. …”
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Method and Optimization of Key Parameters of Soil Organic Matter Detection Based on Pyrolysis Coupled with Artificial Olfaction by Mingwei Li, Xiao Li, Xuexun Li, Wenjun Wang, Yulong Chen, Long Zhou, Xiaomeng Xia

“…Firstly, single-factor experiments were conducted to determine the optimal values of three parameters that can improve the differentiation of pyrolysis gases. …”
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Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning anal... by Abhijeet Das

“…Overall, the LOR, KNN, ANN, and SVM algorithms were all surpassed by the RF algorithm. Additionally, the prediction and simulation results show an increasing tendency in the changes in water quality over the examined time. …”
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Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

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An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…<b>Methods</b>: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. …”
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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

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Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4 by Baohua Zhu, Ziyang Mo, Yi Bao, Xinxin Gan, Linhui Wang

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