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All Journal International Journal of Electrical and Computer Engineering
Bhaskar, Navaneeth
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Computer Science & IT Electrical & Electronics Engineering

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An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications Bhaskar, Navaneeth; Ajithkumar, Aswathy Maruthompilli; Tupe-Waghmare, Priyanka
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2035-2042

Abstract

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.
Co-Authors Ajithkumar, Aswathy Maruthompilli Tupe-Waghmare, Priyanka