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about.html
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<!DOCTYPE html>
<html>
<head>
<title>About - PulmoPred</title>
<link rel = "stylesheet" type = "text/css" href = "css/main.css" />
<style>
.intro {
width:90%;
margin:0 5% 0 5%;
}
.center {
text-align:center;
}
</style>
</head>
<body>
<div class = "section_header">
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<div class ="side_nav"><a href="#utility" class="side_nav"> Utility </a></div>
<div class ="side_nav"><a href="#dataset" class="side_nav"> Dataset </a></div>
<div class ="side_nav"><a href="#features" class="side_nav"> Attributes of spirometry </a></div>
<div class ="side_nav"><a href="#methodology" class="side_nav"> Prediction methodology </a></div>
<div class ="side_nav"><a href="#performance" class="side_nav"> Performance </a></div>
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<center><h1 id="utility">Utility</h1></center>
<ul class="intro">
<li>
Patients and clinicians can use this prediction server to classify obstructive
and non-obstructive diseases.
</li>
<li>
The machine learning (ML) models used in this server were trained on spirometry
data of patients. It not only provides an ouput class label but also gives a
probability of the prediction.
</li>
<li>
Users need to provide only 12 input features for the prediction task.
</li>
<li>
Being a non-invasive test, spirometry features can be easily obtained for patients
of all age groups, especially children.
</li>
</ul>
<center><h1 id="dataset">Dataset</h1></center>
<p class="intro">
The dataset contained spirometry investigation reports of 1314 patients from
Institute of Pulmocare and Research (IPCR), Kolkata diagnosed with obstructive
and non-obstructive diseases. The patients were divided in 2 groups - Group A
and Group B consisting of 1163 and 151 patients respectively. The reports of
the patients diagnosed with obstructive diseases were labelled as positive and
those with non-obstructive diseases were labelled as negative. The reports in
Group A were used for training and testing with cross validation (CV-dataset),
and the reports in Group B were used as blind dataset. A summary of the dataset
is given in <a href="#table1">Table - 1</a>.
</p><br/>
<p class="intro center" id="table1">Table - 1: Summary of patient groups in the dataset.</p>
<table class="form" border="1" cellpadding="3px">
<tr>
<th rowspan="2"></th>
<th>Group A</th>
<th>Group B</th>
</tr>
<tr>
<th>Used for training and testing with 5-fold cross validation</th>
<th>Used as blind dataset for validation</th>
</tr>
<tr>
<td><b>Patient count</b></td>
<td class="center">1163</td>
<td class="center">151</td>
</tr>
<tr>
<td><b>Total number of spirometry reports</b></td>
<td class="center">1172</td>
<td class="center">154</td>
</tr>
<tr>
<td><b>Number of obstructive spirometry reports</b></td>
<td class="center">1006</td>
<td class="center">103</td>
</tr>
<tr>
<td><b>Number of non-obstructive spirometry reports</b></td>
<td class="center">166</td>
<td class="center">51</td>
</tr>
</table><br/>
<center><h1 id="features">Attributes of spirometry</h1></center>
<p class="intro">
In spirometry, patients are asked to take a maximal inspiration and, then, expel
the air forcefully as quickly as possible into a mouthpiece. The test is repeated
following the administration of a bronchodilator. The <i>pre</i> and <i>post</i>
bronchodilator values of the following three metrics were used as input:
</p><br/>
<ul class="intro">
<li>
<b>Forced Vital Capacity (FVC):</b> It is the volume of air exhaled forcefully
and quickly after full inhalation.
</li>
<li>
<b>Forced Expiratory Volume in one second (FEV1):</b> It is the volume of air
expired during the first second of performing the FVC test.
</li>
<li>
<b>Forced Expiratory Flow (FEF25-75):</b> It is the flow (or speed) of air
coming out of the lung during the middle portion of a forced expiration. It is
measured by taking the mean of the flow during the interval 25-75% of FVC.
</li>
</ul><br/>
<p class="intro">
For each of the above tests, there are 4 attributes. Thus, there are a total of 12
attributes.
</p><br/>
<ul class="intro">
<li>
<b>Pre-bronchodilator (Pre-BD) Value:</b> It is the value of the corresponding
metric tested before the administration of bronchodilator.
</li>
<li>
<b>Pre-BD Predicted Ratio:</b> It is the percent ratio of pre-bronchodilator
(pre-BD) value to the predicted value. There is no normal range of values for the
observed metric that is applicable to all individuals in a population. Instead,
comparison is made with an expected value for a patient of a particular gender,
age and physical characteristics. These are called the predicted values.
</li>
<li>
<b>Post-bronchodilator (Post-BD) Value:</b> It is the value of the corresponding
metric tested after the administration of bronchodilator.
</li>
<li>
<b>Post-BD Predicted Ratio:</b> It is the percent ratio of post-bronchodilator
(post-BD) value to the predicted value.
</li>
</ul><br/>
<center><h1 id="methodology">Prediction methodology</h1></center>
<p class="intro">
Supervised machine learning models were developed for the classification task using
Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and Multi-layer
Perceptron (MLP) algorithms. Different performance metrics, such as accuracy, sensitivity,
specificity, F1-score, Matthews correlation coefficient (MCC) and area under receiver
operator characteristic curve (AUROC) were computed and compared. The optimal model was
chosen on the basis of the <i>highest MCC value</i>.
</p><br/>
<p class="intro">
The training dataset used for cross validation was highly imbalanced where the positive
to negative ratio <i>(P:N)</i> was <i>6:1</i>. To handle this imbalance, an undersampling
method was used in which the majority (positive) class samples were randomly divided into
six disjoint (and, exhaustive) subsets. Then the minority (negative) class samples were
concatenated with each positive class subset to obtain six undersampled datasets with
<i>P:N = 1:1</i>. Six models were trained with each undersampled dataset and the performance
metrics were averaged.
</p><br/>
<p class="intro">
The tuning of hyperparameters was performed for each ML algorithm to improve the performance
of the models using <i>grid search</i> technique, which is an exhaustive search using a
parameter grid created by taking the cartesian product of pre-specified sets of values for each
hyperparameter. Hyperparameter optimization was performed separately for both sets of models -
one trained with the whole training set and another with the undersampled datasets. The optimal
model wass saved and used in this prediction server.
</p><br/>
<center><h1 id="performance">Performance</h1></center>
<p class="intro center" id="table2">Table - 2: Performance of models with 5-fold cross validation</p>
<table class="form" border="1" cellpadding="3px">
<tr>
<th>Dataset</th><th>Model</th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th>
<th>F1-score</th><th>MCC</th>
</tr>
<tr>
<td rowspan="4" class="center">Whole training dataset</td>
<td class="center">Support Vector Machine (SVM)</td>
<td class="center">0.835</td>
<td class="center">0.837</td>
<td class="center">0.826</td>
<td class="center">0.897</td>
<td class="center">0.532</td>
</tr>
<tr>
<td class="center">Random Forest (RF)</td>
<td class="center">0.906</td>
<td class="center">0.955</td>
<td class="center">0.609</td>
<td class="center">0.946</td>
<td class="center">0.597</td>
</tr>
<tr>
<td class="center">Naive Bayes (NB)</td>
<td class="center">0.870</td>
<td class="center">0.915</td>
<td class="center">0.602</td>
<td class="center">0.924</td>
<td class="center">0.495</td>
</tr>
<tr>
<td class="center">Multi-layer Perceptron (MLP)</td>
<td class="center">0.918</td>
<td class="center">0.966</td>
<td class="center">0.626</td>
<td class="center">0.953</td>
<td class="center">0.645</td>
</tr>
<tr>
<td rowspan="4" class="center">Under-sampled datasets</td>
<td class="center">Support Vector Machine (SVM)</td>
<td class="center">0.823</td>
<td class="center">0.825</td>
<td class="center">0.821</td>
<td class="center">0.824</td>
<td class="center">0.650</td>
</tr>
<tr>
<td class="center">Random Forest (RF)</td>
<td class="center">0.822</td>
<td class="center">0.832</td>
<td class="center">0.811</td>
<td class="center">0.824</td>
<td class="center">0.647</td>
</tr>
<tr>
<td class="center">Naive Bayes (NB)</td>
<td class="center">0.800</td>
<td class="center">0.864</td>
<td class="center">0.737</td>
<td class="center">0.813</td>
<td class="center">0.607</td>
</tr>
<tr>
<td class="center"><b>Multi-layer Perceptron (MLP)</b></td>
<td class="center"><b>0.837</b></td>
<td class="center"><b>0.853</b></td>
<td class="center"><b>0.822</b></td>
<td class="center"><b>0.841</b></td>
<td class="center"><b>0.682</b></td>
</tr>
</table><br/>
<p class="intro">
The MLP model trained with the under-sampled datsets showed optimal performance with MCC of 0.68
and accuracy of 83.7% (<a href="#table2">Table - 2</a>). This model is used in this prediction
server. The hyperparameters chosen by the grid-search algorithm for this MLP model used two hidden
layer architecture - 100 nodes in the first hidden layer followed by 100 nodes in the second. The
input and output layers used 12 and 1 nodes respectively. An <i>"adam"</i> weight
optimizer and a <i>rectified linear unit (ReLU)</i> activation function with constant learning rate
of <i>0.001</i> was used. The ROC plot of the different models are given in
<a href="#figure1">Figure - 1</a>. The performance of the models on blind dataset (Group - B) is
given in <a href="#table3">Table - 3</a>.
</p><br/>
<center><img id="figure1" src="public/roc_plot.jpeg" height="700px" width="auto" /></center>
<p class="intro center">
Figure - 1: Receiver Operator Characteristic (ROC) plot of different models.
(<i>σ</i>-standard deviation)
</p><br/>
<p class="intro center" id="table3">Table - 3: Performance of models on predicting the validation dataset</p>
<table class="form" border="1" cellpadding="3px">
<tr>
<th>Training Dataset</th><th>Model</th><th>Accuracy</th><th>Sensitivity</th>
<th>Specificity</th><th>F1-score</th><th>MCC</th>
</tr>
<tr>
<td rowspan="4" class="center">Whole training dataset</td>
<td class="center">Support Vector Machine (SVM)</td>
<td class="center">0.853</td>
<td class="center">0.897</td>
<td class="center">0.765</td>
<td class="center">0.891</td>
<td class="center">0.667</td>
</tr>
<tr>
<td class="center">Random Forest (RF)</td>
<td class="center">0.835</td>
<td class="center">0.971</td>
<td class="center">0.561</td>
<td class="center">0.887</td>
<td class="center">0.619</td>
</tr>
<tr>
<td class="center">Naive Bayes (NB)</td>
<td class="center">0.823</td>
<td class="center">0.944</td>
<td class="center">0.580</td>
<td class="center">0.877</td>
<td class="center">0.586</td>
</tr>
<tr>
<td class="center">Multi-layer Perceptron (MLP)</td>
<td class="center">0.857</td>
<td class="center">0.986</td>
<td class="center">0.596</td>
<td class="center">0.902</td>
<td class="center">0.677</td>
</tr>
<tr>
<td rowspan="4" class="center">Under-sampled datasets</td>
<td class="center">Support Vector Machine (SVM)</td>
<td class="center">0.854</td>
<td class="center">0.898</td>
<td class="center">0.766</td>
<td class="center">0.892</td>
<td class="center">0.669</td>
</tr>
<tr>
<td class="center">Random Forest (RF)</td>
<td class="center">0.862</td>
<td class="center">0.902</td>
<td class="center">0.781</td>
<td class="center">0.897</td>
<td class="center">0.687</td>
</tr>
<tr>
<td class="center">Naive Bayes (NB)</td>
<td class="center">0.855</td>
<td class="center">0.926</td>
<td class="center">0.712</td>
<td class="center">0.895</td>
<td class="center">0.665</td>
</tr>
<tr>
<td class="center">Multi-layer Perceptron (MLP)</td>
<td class="center">0.849</td>
<td class="center">0.886</td>
<td class="center">0.774</td>
<td class="center">0.887</td>
<td class="center">0.663</td>
</tr>
</table>
</div>
<div style="clear:both;"><br/><hr/></div>
<p style="font-size:0.8em;text-align:center;">
Bhattacharjee S. <i>et al.</i>, <i>J Comput Sci</i> (2022), 63:101768. <i>doi:</i>
<a href="https://doi.org/10.1016/j.jocs.2022.101768" target="_blank">10.1016/j.jocs.2022.101768</a>.
Please contact Dr. Sudipto Saha (<a href="mailto:[email protected]">[email protected]</a>)
regarding any further queries.
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