A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset
Date
2022-05-30
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh
Abstract
The chronic cardiac condition myocardial infarction (heart failure) is characterized by decreased blood supply to the body as a result of the heart muscles’ impaired contractile
properties. Patients with heart failure, like those with any other cardiac disorder, have
difficulty performing daily activities and have a shorter life expectancy, with the vast majority of cases resulting in death at some point during the patient’s lifetime. Treatment
outcomes and patient quality of life improve significantly when patients with heart failure
are identified early and are likely to survive. As a result, machine learning techniques can
be extremely beneficial in this situation because they can be used to predict the survival of
heart failure patients in advance, allowing patients to receive the most appropriate treat-
ment at the earliest possible stage. As a result, six supervised machine learning algorithms
were applied to a dataset of 299 people from the University of California, Irvine Machine
Learning Repository in order to predict their chances of surviving heart failure. There
were a variety of algorithms used in this study including Decision Tree Classifier, Logistic
Regression, Gaussian Nave Bayes, Random Forest Classifier, K-Nearest Neighbors, and
Support Vector Machine, among others. Prior to scaling the data, a preprocessing step was
carried out, and both the standard and min-max scaling methods were employed. When it
came to optimizing the hyperparameters, the techniques grid-search cross validation and
random search cross validation were combined. Data resampling techniques such as the
edited nearest neighbor (SMOTE-ENN) and synthetic minority oversampling (SMOTE)
data resampling are also employed (SMOTE-ENN). It has been thoroughly compared and
analyzed the outcomes of all of the different approaches. As a result of these findings,
the Random Forest Classifier (RFC) outperforms all other approaches, achieving a test
accuracy of 90 percent when compared to the other approaches when SMOTE-ENN and
the standard scaling technique are employed. With the help of an imbalanced dataset, this
comprehensive investigation vividly illustrates the application and compatibility of sev-
eral machine learning algorithms. Among the methods for improving the performance of
machine learning algorithms discussed in this investigation are the SMOTE-ENN algo-
rithm and hyperparameter optimization.
Description
Supervised by
Mr. Fahim Faisal,
Assistant Professor,
Co-Supervisor
Mr. Mirza Muntasir Nishat,
Assistant Professor,
Department of Electrical and Electronic Engineering (EEE),
Islamic University of Technology (IUT),
Board Bazar, Gazipur-1704, Bangladesh.
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.
Keywords
Machine learning, hyperparameter tuning, smapling, prediction
Citation
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