A Comprehensive Approach for Using Hybrid Ensemble Methods for Diabetes Detection

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Author(s): Md Sakir Ahmed (Department of Computer Applications, Assam Don Bosco University, India)and Abhijit Bora (Assam Don Bosco University, India)
Copyright: 2024
Pages: 15
Source title: Critical Approaches to Data Engineering Systems and Analysis
Source Author(s)/Editor(s): Abhijit Bora (Assam Don Bosco University, India), Papul Changmai (Assam Don Bosco University, India)and Mrutyunjay Maharana (Xi’an Jiatong University, China)
DOI: 10.4018/979-8-3693-2260-4.ch001

Keywords: Computer Engineering / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

This study is focused on the possible application of hybrid models as well as their usage in the detection of diabetes. This study focuses on various machine learning algorithms like Decision Trees, Random Forests, Logistic Regression, K-nearest neighbor, Support Vector Machines, Gaussian Naive Bayes, Adaptive Boosting Classifier, and Extreme Gradient Boosting as well as the usage of Stacking Classifier for the preparation of the hybrid model. An in-depth analysis was also made during this study to compare the traditional approach with the hybrid approach. Moreover, the usage of data augmentation and its application during an analysis has also been discussed along with the application of hyperparameter tuning and cross-validation during training of the various models.

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A Comprehensive Approach for Using Hybrid Ensemble Methods for Diabetes Detection

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