From 06b862b1a1c381ee782458dda6fe0521bb5f7f52 Mon Sep 17 00:00:00 2001
From: Felix Mucha <3016498@stud.hs-mannheim.de>
Date: Wed, 12 Jun 2024 16:53:37 +0200
Subject: [PATCH 1/2] readme finished
---
README.md | 23 ++++++++++++++++++++---
1 file changed, 20 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index f37305d..1abe34b 100644
--- a/README.md
+++ b/README.md
@@ -145,6 +145,8 @@ The colour scale represents the correlation between the two types of categorizat
- Blue (low)
- Red (high)
+Notably, the age group between 60-70 in SB shows a noticeably higher correlation and will therefore be considered in our hypothesis analysis. The other groups align mostly as expected, with a slight increase in correlation observed with age.
+
The exact procedure for creating the matrix can be found in the notebook [demographic_plots.ipynb](notebooks/demographic_plots.ipynb).
@@ -192,17 +194,32 @@ With those Classifiers, the hypothesis can be proven, that a classifier is able
The sample size in the study conducted may also play a role in the significance of the frequency.
### Noise reduction
-Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise.
+Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise. For noise reduction, the built-in noise reduction function from NeuroKit2 `ecg_clean` was utilized for all data due to considerations of time performance.
How the noise reduction was performed in detail can be seen in the following notebook: [noise_reduction.ipynb](notebooks/noise_reduction.ipynb)
### Features
-The detection ability of the NeuroKit2 library is tested to detect features in the ECG dataset. Those features are important for the training of the model in order to detect the different diagnostic groups. The features are generated using the NeuroKit2 library.
+The detection ability of the NeuroKit2 library is tested to detect features in the ECG dataset. Those features are important for the training of the model in order to detect the different diagnostic groups. The features are detected using the NeuroKit2 library.
+
+For the training, the features considered are:
+- ventricular rate
+- atrial rate
+- T axis
+- R axis
+- Q peak amplitude
+- QT length
+- QRS duration
+- QRS count
+- gender
+- age
+
+The selection of features was informed by an analysis presented in a paper (source: https://rdcu.be/dH2jI, last accessed: 15.05.2024), where various feature sets were evaluated. These features were chosen for their optimal balance between performance and significance.
The exact process can be found in the notebook: [features_detection.ipynb](notebooks/features_detection.ipynb).
### ML-models
-First, the grid was tested to find the best model which was then trained to identify the best hyperparameters out of it. That way, an accuracy of 83 % was achieved with the XGBoost classifier. The Gradient Boosting Tree Classifier had an accuracy of 82%.
+For machine learning, the initial step involved tailoring the features for the models, followed by employing a grid search to identify the best hyperparameters. This approach led to the highest performance being achieved by the Extreme Gradient Boosting (XGBoost) model, which attained an accuracy of 83%. Additionally, a Gradient Boosting Tree model was evaluated using the same procedure and achieved an accuracy of 82%. The selection of these models was influenced by the team's own experience and the performance metrics highlighted in the paper (source: https://rdcu.be/dH2jI, last accessed: 15.05.2024). The models have also been evaluated, and it is noticeable that some features, like the ventricular rate, are shown to be more important than other features.
+
The detailed procedures can be found in the following notebooks:
[ml_xgboost.ipynb](notebooks/ml_xgboost.ipynb)
[ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
From 2f89518bbf1ce661fc504f41fecb4ad80b65749e Mon Sep 17 00:00:00 2001
From: Felix Mucha <3016498@stud.hs-mannheim.de>
Date: Wed, 12 Jun 2024 17:19:27 +0200
Subject: [PATCH 2/2] added f1 score
---
notebooks/ml_grad_boost_tree.ipynb | 74 +++++++++++++++++++-----------
notebooks/ml_xgboost.ipynb | 63 +++++++++++++++----------
2 files changed, 85 insertions(+), 52 deletions(-)
diff --git a/notebooks/ml_grad_boost_tree.ipynb b/notebooks/ml_grad_boost_tree.ipynb
index 341b439..fcddf8f 100644
--- a/notebooks/ml_grad_boost_tree.ipynb
+++ b/notebooks/ml_grad_boost_tree.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -21,7 +21,7 @@
"import matplotlib.pyplot as plt\n",
"import xgboost as xgb\n",
"from sklearn.model_selection import GridSearchCV\n",
- "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import confusion_matrix, f1_score\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.metrics import accuracy_score\n",
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -63,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -138,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -151,7 +151,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -171,29 +171,29 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: total: 3min 28s\n",
- "Wall time: 4min 16s\n"
+ "CPU times: total: 2min 49s\n",
+ "Wall time: 4min 28s\n"
]
},
{
"data": {
"text/html": [
- "
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GradientBoostingClassifier()
"
+ "
GradientBoostingClassifier()
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