Journal ArticleDOI
An improved support vector machine-based diabetic readmission prediction.
TLDR
A novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data is presented, which outperforms other popular algorithms in identifying diabetic patients who may be readmitted.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2018-11-01. It has received 85 citations till now. The article focuses on the topics: Decision tree.read more
Citations
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Journal ArticleDOI
A stacking-based ensemble learning method for earthquake casualty prediction
TL;DR: It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy.
Journal ArticleDOI
Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
TL;DR: A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data and shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal.
Journal ArticleDOI
Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques: An Algorithmic Approach for Early Screening of Diabetes:
P. Nagaraj,P. Deepalakshmi +1 more
TL;DR: The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy, and shows that the deep Learning model gives more efficiency for diabetes prediction.
Journal ArticleDOI
An XGBoost-based casualty prediction method for terrorist attacks
TL;DR: This study is the first to apply machine learning in the management of terrorist attacks, which can provide early warning and decision support information for terrorist attack management.
Journal ArticleDOI
Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.
TL;DR: If present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated, however, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.
References
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Journal ArticleDOI
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal ArticleDOI
Global estimates of the prevalence of diabetes for 2010 and 2030.
TL;DR: These predictions, based on a larger number of studies than previous estimates, indicate a growing burden of diabetes, particularly in developing countries.
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Selection bias in gene extraction on the basis of microarray gene-expression data.
TL;DR: It is demonstrated that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.
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Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification
TL;DR: In this article, the authors address statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called "class prediction", which includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.