Erweiterungen
klara
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"# Demographic Plots\n",
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"# Demographic Plots\n",
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"This Notebook is used to read the data from the pickle files and to create a dataframe with the demographic data.\n",
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"This notebook is used to read the data from the pickle files and to create a dataframe with the demographic data.\n",
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"With this data we can create a plots to show the distribution of the demographic data."
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"With this data we can create a plots to show the distribution of the demographic data."
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"source": [
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"source": [
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"# Set path to data\n",
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"# Set path to data\n",
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"path = \"C:/Studium/dsa/data\"\n",
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"path = \"C:/Studium/dsa/data\"\n",
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\""
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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"#C:\\Users\\klara\\projects\\DSA\\a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0"
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Hypothesis\n",
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"This notebook is used to read the data from the pickle files and to test the hypothesis that in the age group of 60-70 the frequency of a sinus bradycardia is significantly higher than in the other age groups.\n",
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"For that instance the chi-squared test is used."
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]
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"import pickle\n",
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"from scipy.stats import chi2_contingency\n",
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"from data_helper import *\n"
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]
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading SB\n",
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"Length of SB: 50\n",
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"Reading AFIB\n",
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"Length of AFIB: 27\n",
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"Reading GSVT\n",
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"Length of GSVT: 0\n",
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"Reading SR\n",
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"Length of SR: 13\n",
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"Chi-Square Statistic: 38.266574797751275\n",
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"P-value: 0.0004730210823940083\n",
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"Chi-Square Statistic for SB in 60-70 vs others: 1.4858035714285718\n",
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"P-value for SB in 60-70 vs others: 0.22286870264719977\n"
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]
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}
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],
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"source": [
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"#path = \"C:/Studium/dsa/data\"\n",
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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"path = \"C:/Users/klara/projects/DSA/data\"\n",
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"\n",
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"categories_dict = {\n",
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"'SB': [426177001],\n",
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"'AFIB': [164889003, 164890007],\n",
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"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"'SR': [426783006, 427393009]\n",
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"}\n",
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"\n",
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"data = {}\n",
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"for cat_name in categories_dict.keys():\n",
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" print(f\"Reading {cat_name}\")\n",
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" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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" records = pickle.load(f)\n",
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" data[cat_name] = records\n",
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" print(f\"Length of {cat_name}: {len(records)}\")\n",
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"\n",
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"data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
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"for cat_name, records in data.items():\n",
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" for record in records:\n",
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" age = record.comments[0].split(' ')[1]\n",
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" sex = record.comments[1].split(' ')[1]\n",
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" if age == 'NaN' or sex == 'NaN':\n",
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" continue\n",
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" # cut Age: from alter string \n",
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" data_demographic['age'].append(int(age))\n",
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" data_demographic['diag'].append(cat_name)\n",
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" data_demographic['gender'].append(sex)\n",
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"\n",
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"df_dgc = pd.DataFrame(data_demographic)\n",
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"\n",
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"# 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)\n",
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"corr_matrix_age_diag= pd.crosstab(df_dgc['age_group'], df_dgc['diag'])\n",
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"\n",
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"# Chi-square test\n",
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"chi2, p, _, _ = chi2_contingency(corr_matrix_age_diag)\n",
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"\n",
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"# Difference between observed and expected frequencies\n",
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"print(f\"Chi-Square Statistic: {chi2}\")\n",
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"print(f\"P-value: {p}\")\n",
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"\n",
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"# Check if SB (Sinusbradykardie) has a significantly higher frequency in the 60-70 age group\n",
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"sb_60_70 = corr_matrix_age_diag.loc[pd.Interval(60, 70, closed='right'), 'SB']\n",
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"sb_other = corr_matrix_age_diag.drop(pd.Interval(60, 70, closed='right')).sum()['SB']\n",
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"total_60_70 = corr_matrix_age_diag.loc[pd.Interval(60, 70, closed='right')].sum()\n",
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"total_other = corr_matrix_age_diag.drop(pd.Interval(60, 70, closed='right')).sum().sum()\n",
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"\n",
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"# Frequency table for the specific Chi-Square test\n",
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"observed = [[sb_60_70, total_60_70 - sb_60_70], [sb_other, total_other - sb_other]]\n",
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"chi2_sb, p_sb = chi2_contingency(observed)[:2]\n",
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"\n",
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"\n",
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"print(f\"Chi-Square Statistic for SB in 60-70 vs others: {chi2_sb}\")\n",
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"print(f\"P-value for SB in 60-70 vs others: {p_sb}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The results can be interpreted as followed:\n",
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"\n",
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"- The first value returned is the Chi-Square Statistic that shows the difference between the observed and the expected frequencies. Here, a bigger number indicates a bigger difference. The p-value shows the probability of this difference being statistically significant. If the p-value is below the significance level of 0.05, the difference is significant.\n",
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"\n",
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"- The Chi-Square Statistic for sinus bradycardia in the age group 60-70 compared to the other age groups, is a value that shows whether there is a significant difference in the frequency of sinus bradycardia in the age group 60-70 in comparison to the other age groups. If the p-value is smaller than the significance level of 0.05, the difference in the frequency between the age group 60-70 and the other age groups is significant."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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