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عنوان
Prediction of Diabetic Patient Readmission Using Hybrid Ensemble Learning

پدید آورنده
Ghazo, Esraa

موضوع
Industrial engineering

رده

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

NATIONAL BIBLIOGRAPHY NUMBER

Number
TL50926

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Prediction of Diabetic Patient Readmission Using Hybrid Ensemble Learning
General Material Designation
[Thesis]
First Statement of Responsibility
Ghazo, Esraa
Subsequent Statement of Responsibility
Khasawneh, Mohammad

.PUBLICATION, DISTRIBUTION, ETC

Name of Publisher, Distributor, etc.
State University of New York at Binghamton
Date of Publication, Distribution, etc.
2019

GENERAL NOTES

Text of Note
139 p.

DISSERTATION (THESIS) NOTE

Dissertation or thesis details and type of degree
M.S.
Body granting the degree
State University of New York at Binghamton
Text preceding or following the note
2019

SUMMARY OR ABSTRACT

Text of Note
This thesis introduces the hybrid ensemble learning technique that combines and fully exploits different machine learning classifiers with their optimized hyperparameters such as SVM, GNB, PNN, MLP, DT, RF, KNN, LR and Keras (deep learning API). By using the output predictions for these classification algorithms as input for the ensemble learning algorithms such as boosting, bagging, voting. The output predictions for these ensemble algorithms also will be used as input prediction for the stacking method to produce a more robust model, give more efficient final prediction and achieve the best performance measures. Used on a binary classification dataset, this technique includes the combination of various first-level predictive models to produce a second-level model and then to produce a third-level model which leads to outperform all of them. Techniques such as dimensionality reduction using stepwise discriminant analysis, hyperparameter optimization using grid search, among others, have been adopted. This hybrid model is applied on the dataset before features selection and after features selection, using two techniques the genetic algorithm and filter algorithm. The hybrid model attained the highest accuracy, sensitivity, and AUC of 79.1%, 79.3%, and 81.3% respectively using 10-fold cross-validation with the genetic algorithm for features selection. T-test was also done with and without features selection to prove that the results are significant.

UNCONTROLLED SUBJECT TERMS

Subject Term
Industrial engineering

PERSONAL NAME - PRIMARY RESPONSIBILITY

Ghazo, Esraa

PERSONAL NAME - SECONDARY RESPONSIBILITY

Khasawneh, Mohammad

CORPORATE BODY NAME - SECONDARY RESPONSIBILITY

State University of New York at Binghamton

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

p

[Thesis]
276903

a
Y

Proposal/Bug Report

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