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{
"cells": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Demographic Plots\n",
"This Notebook is used to read the data from the pickle files and to create a dataframe with the demographic data.\n",
"With this data we can create a plots to show the distribution of the demographic data."
]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
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"import pickle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set path to data\n",
"path = \"C:/Studium/dsa/data\"\n",
"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
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]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading SB\n",
"Length of SB: 16559\n",
"Reading AFIB\n",
"Length of AFIB: 9839\n",
"Reading GSVT\n",
"Length of GSVT: 948\n",
"Reading SR\n",
"Length of SR: 9720\n"
]
}
],
"source": [
"categories_dict = {\n",
"'SB': [426177001],\n",
"'AFIB': [164889003, 164890007],\n",
"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
"'SR': [426783006, 427393009]\n",
"}\n",
"\n",
"data = {}\n",
"for cat_name in categories_dict.keys():\n",
" print(f\"Reading {cat_name}\")\n",
" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
" records = pickle.load(f)\n",
" data[cat_name] = records\n",
" print(f\"Length of {cat_name}: {len(records)}\")\n",
"\n",
"data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
"for cat_name, records in data.items():\n",
" for record in records:\n",
" age = record.comments[0].split(' ')[1]\n",
" sex = record.comments[1].split(' ')[1]\n",
" if age == 'NaN' or sex == 'NaN':\n",
" continue\n",
" # cut Age: from alter string \n",
" data_demographic['age'].append(int(age))\n",
" data_demographic['diag'].append(cat_name)\n",
" data_demographic['gender'].append(sex)\n",
"\n",
"df_dgc = pd.DataFrame(data_demographic)\n",
"\n",
"# Change from group to category\n",
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"age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
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"df_dgc['age_group'] = pd.cut(df_dgc['age'], bins=age_categories)"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot Data"
]
},
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{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"corr_matrix_age_diag= pd.crosstab(df_dgc['age_group'], df_dgc['diag'])\n",
"\n",
"# Plot the correlation matrix\n",
"sns.heatmap(corr_matrix_age_diag, annot=True, cmap='coolwarm', fmt='d')\n",
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"plt.title('Korrelationsmatrix von Altersgruppen und Diagnosen', fontsize=16)\n",
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"plt.xlabel('Diagnose')\n",
"plt.ylabel('Altersgruppe')\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correlation matrix of age groups and diagnoses.This matrix describes the four diagnosis groupings on the horizontal axis and the age groupings in decades steps on the vertical axis. The color scale blue (low) to red (high) describes the correlation of the two categorization types."
]
},
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# cut out sex 'unknown' (because only one occurence)\n",
"df_dgc_bineary = df_dgc[df_dgc['gender'] != 'Unknown']\n",
"# Correlation matrix\n",
"corr_matrix_sex_diag = pd.crosstab(df_dgc_bineary['gender'], df_dgc_bineary['diag'])\n",
"\n",
"# Plot the correlation matrix\n",
"sns.heatmap(corr_matrix_sex_diag, annot=True, cmap='coolwarm', fmt='d')\n",
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"plt.title('Korrelationsmatrix von Geschlecht und Diagnosen', fontsize=16)\n",
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"plt.xlabel('Diagnose')\n",
"plt.ylabel('Geschlecht')\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correlation matrix of genders and diagnoses. This matrix describes the four diagnosis groupings on the horizontal axis and the gender in decades steps on the vertical axis. The color scale blue (low) to red (high) describes the correlation of the two categorization types."
]
},
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{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 4 subplots for each diagnosis a histrgramm for the age\n",
"fig, axs = plt.subplots(2, 2)\n",
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"fig.suptitle('Histogramm der Altersverteilung', fontsize=16)\n",
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"for i, cat_name in enumerate(categories_dict.keys()):\n",
" ax = axs[i // 2, i % 2]\n",
" df_dgc[df_dgc['diag'] == cat_name]['age'].hist(ax=ax)\n",
" ax.set_title(cat_name)\n",
" ax.set_xlabel('Alter')\n",
" ax.set_ylabel('Anzahl')\n",
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" # add some space between the subplots\n",
"plt.tight_layout()\n",
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"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram of the age distribution. Breakdown of grouped diagnoses by age group and absolute incidence of diagnoses."
]
},
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{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# a barplot for each category with the age grpuoped besides each other\n",
"fig, ax = plt.subplots()\n",
"sns.countplot(data=df_dgc_bineary, x='diag', hue='gender', ax=ax)\n",
"plt.title('Anzahl der Diagnosen nach Geschlecht')\n",
"plt.xlabel('Diagnose')\n",
"plt.ylabel('Anzahl')\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Barplot to visualize the distribution of diagnoses by gender. Where blue is female and orange is male."
]
},
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{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
2024-05-08 17:45:29 +02:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# barplot how many diagnosis are in each age group\n",
"fig, ax = plt.subplots()\n",
"sns.countplot(data=df_dgc, x='age_group', hue='diag', ax=ax)\n",
"plt.title('Anzahl der Diagnosen nach Altersgruppen')\n",
"plt.xlabel('Altersgruppe')\n",
"plt.ylabel('Anzahl')\n",
"plt.show()"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Barplot to show the distribution of diagnoses based on the age groupings. The difference in the incidence of the various diseases can be clearly seen here."
]
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
