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<!DOCTYPE html>
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<title>DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis</title>
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<h1 class="title is-1 publication-title">DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://steve-zeyu-zhang.github.io/" target="_blank">Zeyu Zhang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=rUJ9DVAAAAAJ&hl=en" target="_blank">Khandaker Asif Ahmed</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://staffportal.curtin.edu.au/staff/profile/view/rakibul-hasan-145a1046/" target="_blank">Md Rakibul Hasan</a><sup>3</sup>,
</span>
</span>
<span class="author-block">
<a href="https://staffportal.curtin.edu.au/staff/profile/view/tom-gedeon-5e48a1fd/" target="_blank">Tom Gedeon</a><sup>1,3,4</sup>,
</span>
<span class="author-block">
<a href="https://sites.google.com/view/zakirh?pli=1" target="_blank">Md Zakir Hossain</a><sup>1,2,3</sup>
</span>
</div>
<p style="height: 10px;"> </p>
<div class="is-size-5 publication-authors">
<span class="author-block"><font size="4"><sup>1</sup> <a href="https://www.anu.edu.au/" target="_blank">The Australian National University</a></font></span>
<span class="author-block"><font size="4"><sup>2</sup> <a href="https://www.csiro.au/en/" target="_blank">CSIRO</a></font></span><br>
<span class="author-block"><font size="4"><sup>3</sup> <a href="https://www.curtin.edu.au/" target="_blank">Curtin University</a></font></span>
<span class="author-block"><font size="4"><sup>4</sup> <a href="https://uni-obuda.hu/en/" target="_blank">Óbuda University</a></font></span>
<p style="height: 10px;"> </p>
<span class="author-block"><font size="5"><b><a href="https://aciids.pwr.edu.pl/2024/" target="_blank">ACIIDS 2024</a></b></font></span><br>
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<span>arXiv</span>
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class="external-link button is-normal is-rounded is-dark">
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</section>
<div class="news">
<h2><font size="4"> <b>News:</b> </font></h2>
<ul>
<li><b>(3/2/2024)</b> 🎉 Our paper has been accepted to <a href="https://aciids.pwr.edu.pl/2024/"><b>ACIIDS 2024</b></a>!</li>
</ul>
</div>
<p style="height: 50px;"> </p>
<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Diabetes, resulting from inadequate insulin production or
utilization, causes extensive harm to the body. Existing diagnostic methods are often invasive and come with drawbacks, such as cost constraints.
Although there are machine learning models like Classwise k Nearest
Neighbor (CkNN) and General Regression Neural Network (GRNN),
they struggle with imbalanced data and result in underperformance.
Leveraging advancements in sensor technology and machine learning,
we propose a non-invasive diabetes diagnosis using a Back Propagation
Neural Network (BPNN) with batch normalization, incorporating data
re-sampling and normalization for class balancing. Our method addresses
existing challenges such as limited performance associated with traditional machine learning. Experimental results on three datasets show
significant improvements in overall accuracy, sensitivity, and specificity
compared to traditional methods. Notably, we achieve accuracies of <b>89.81%</b>
in Pima diabetes dataset, <b>75.49%</b> in CDC BRFSS2015 dataset, and
<b>95.28%</b> in Mesra Diabetes dataset. This underscores the potential of advanced deep learning models, including Transformers, for robust diabetes
diagnosis.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<section class="hero is-small">
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<h2 class="title is-3">Methodology</h2>
<div class="image-container">
<center>
<img src="static/images/diabetes.svg" style="width: 50%;">
<div class="image-description">
<p style="height: 10px;"> </p><br>
<p>Workflow of Proposed Method: The pipeline encompasses crucial components,
including data undersampling to address class imbalance in the dataset. The Workflow of our proposed method illustrates the data scaling procedure for effective feature
normalization. The backbone of the pipeline consists of a Back Propagation Neural
Network (BPNN) architecture, enhanced with batch normalization, to facilitate automatic diabetes diagnosis. This comprehensive pipeline demonstrates potential for
accurate and automated diabetes classification.</p><br>
</div>
</center>
</div>
<div class="image-container">
<center>
<img src="static/images/BPNN.svg" style="width: 40%;">
<div class="image-description">
<p style="height: 10px;"> </p><br>
<p>Back Propagation Neural Network (BPNN) model, adopting a five-fold crossvalidation approach to assess its performance and ensure robustness in the evaluation process.</p><br>
</div>
</center>
</div>
</div>
</div>
</section>
<section class="hero is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Visualization</h2>
<div class="image-container">
<center>
<img src="static/images/scale.png" style="width: 50%;">
<div class="image-description">
<p style="height: 10px;"> </p><br>
<p>The figure displays the feature distributions for diabetes diagnosis in the dataset
before (top sub-figure) and after (bottom sub-figure) scaling using standardization.
Standardization has successfully transformed the features to a comparable magnitude,
resulting in a more uniform distribution, facilitating the training process and enhancing
the performance of the Back Propagated diabetes diagnosis model.</p><br>
</div>
</center>
</div>
<div class="image-container">
<center>
<img src="static/images/2d.png" style="width: 60%;">
<div class="image-description">
<p style="height: 10px;"> </p><br>
<p>The plot compares the distribution of positive and negative samples using two
methods, PCA (linear dimensionality reduction) and t-SNE (nonlinear dimensionality
reduction), providing a comprehensive visualization of their distribution in the Pima dataset.</p><br>
</div>
</center>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Experiments</h2>
<div class="image-container">
<center>
<img src="static/images/table.svg" style="width: 70%;">
<div class="image-description">
<p style="height: 10px;"> </p><br>
<p>Comparative results on different datasets with various models. The cells with
'-' indicate that certain comparative studies did not assess their models on specific
datasets.</p><br>
</div>
</center>
</div>
</div>
</div>
</section>
<!-- Paper poster -->
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<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zhang2024deep,
title={A deep learning approach to diabetes diagnosis},
author={Zhang, Zeyu and Ahmed, Khandaker Asif and Hasan, Md Rakibul and Gedeon, Tom and Hossain, Md Zakir},
booktitle={Asian Conference on Intelligent Information and Database Systems},
pages={87--99},
year={2024},
organization={Springer}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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