Cargando…

Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

PURPOSE: This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. PATIENTS AND METHODS: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 case...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Ke, Tian, Jing, Zheng, Chu, Yang, Hong, Ren, Jia, Li, Chenhao, Han, Qinghua, Zhang, Yanbo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Dove 2021
Materias:	Original Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206455/ https://www.ncbi.nlm.nih.gov/pubmed/34149290 http://dx.doi.org/10.2147/RMHP.S310295

Ejemplares similares

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta
por: Wang, Xuchun, et al.
Publicado: (2023)

Kaldere enn i verdensrommet
Publicado: (2009)

SMOTE-CD: SMOTE for compositional data
por: Nguyen, Teo, et al.
Publicado: (2023)

Enn tystörm ytin kadotti s hkön
Publicado: (2009)

Signaler kan gå raskere enn lyset
por: Bøkstedt, H
Publicado: (1982)

Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model
por: Zheng, Chu, et al.
Publicado: (2021)

Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time
por: Yang, Hong, et al.
Publicado: (2021)

IEEE-INNS-ENNS International Joint Conference on Neural Networks
Publicado: (2000)

Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection
por: Seo, Jae-Hyun, et al.
Publicado: (2018)

Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms
por: Zhao, Zhihong, et al.
Publicado: (2022)

Data on a coupled ENN / t-SNE model for soil liquefaction evaluation
por: Atangana Njock, Pierre Guy, et al.
Publicado: (2020)

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset
por: Swana, Elsie Fezeka, et al.
Publicado: (2022)

A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure
por: Han, Qiang, et al.
Publicado: (2020)

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms
por: Hassanzadeh, Roghayyeh, et al.
Publicado: (2023)

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge
por: Tian, Jing, et al.
Publicado: (2023)

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China
por: Wang, Yumiao, et al.
Publicado: (2019)

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction
por: Marques, Yuri Bento, et al.
Publicado: (2016)

SMOTE for high-dimensional class-imbalanced data
por: Blagus, Rok, et al.
Publicado: (2013)

Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction
por: Marques, Yuri Bento, et al.
Publicado: (2017)

Malicious Domain Detection Based on K-means and SMOTE
por: Wang, Qing, et al.
Publicado: (2020)

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project
por: Alghamdi, Manal, et al.
Publicado: (2017)

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
por: Wang, Qi, et al.
Publicado: (2017)

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks
por: Popoola, Segun I., et al.
Publicado: (2021)

Research on expansion and classification of imbalanced data based on SMOTE algorithm
por: Wang, Shujuan, et al.
Publicado: (2021)

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model
por: Tian, Jing, et al.
Publicado: (2021)

LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
por: Nakamura, Munehiro, et al.
Publicado: (2013)

Enhancement of conformational B-cell epitope prediction using CluSMOTE
por: Solihah, Binti, et al.
Publicado: (2020)

Classification and prediction of spinal disease based on the SMOTE-RFE-XGBoost model
por: Zhang, Biao, et al.
Publicado: (2023)

A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes
por: Ismail, Eman, et al.
Publicado: (2023)

Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
por: Zheng, Chu, et al.
Publicado: (2021)

Impact of Combined Processes Involving Ultrasound and Pulsed Electric Fields on ENNs, and OTA Mitigation of an Orange Juice-Milk Based Beverage
por: Sebastià, Albert, et al.
Publicado: (2023)

CHF-PROM: validation of a patient-reported outcome measure for patients with chronic heart failure
por: Tian, Jing, et al.
Publicado: (2018)

Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms
por: Sowjanya, A. Mary, et al.
Publicado: (2022)

Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach
por: Karamti, Hanen, et al.
Publicado: (2023)

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm
por: Tan, Xiaopeng, et al.
Publicado: (2019)

Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19
por: Turlapati, Venkata Pavan Kumar, et al.
Publicado: (2020)

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations
por: Morrill, James, et al.
Publicado: (2021)

Gas–Liquid Two-Phase Flow Pattern Identification of a Centrifugal Pump Based on SMOTE and Artificial Neural Network
por: He, Denghui, et al.
Publicado: (2021)

Analysis Of Re-Hospitalizations For Patients With Heart Failure Caused By Coronary Heart Disease: Data Of First Event And Recurrent Event
por: Tian, Jing, et al.
Publicado: (2019)

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques
por: Tripoliti, Evanthia E., et al.
Publicado: (2016)

Cannot write session to /tmp/vufind_sessions/sess_s87gn0a2gnn8vnhslq62b7o4bd