{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n",
    "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",
    "For that instance the chi-squared test is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import sys\n",
    "\n",
    "\n",
    "from scipy.stats import chi2_contingency\n",
    "sys.path.append('../scripts')\n",
    "import data_helper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading GSVT\n",
      "Reading AFIB\n",
      "Reading SR\n",
      "Reading SB\n",
      "Number of patients per category:\n",
      "age: 37011\n",
      "diag: 37011\n",
      "gender: 37011\n"
     ]
    }
   ],
   "source": [
    "data = data_helper.load_data(only_demographic=True)\n",
    "\n",
    "print(\"Number of patients per category:\")\n",
    "for cat_name in data.keys():\n",
    "    print(f\"{cat_name}: {len(data[cat_name])}\")\n",
    "\n",
    "df_dgc = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients in a diagnosis category: SB      15826\n",
      "SR      10426\n",
      "AFIB     9756\n",
      "GSVT     1003\n",
      "Name: diag, dtype: int64\n",
      "Min number of patients in a diagnosis category: 1003\n",
      "unique values in the diagnosis category: ['GSVT' 'AFIB' 'SR' 'SB']\n",
      "GSVT    1003\n",
      "AFIB    1003\n",
      "SR      1003\n",
      "SB      1003\n",
      "Name: diag, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# get number of patients in a diagnosis category\n",
    "num_patients = df_dgc['diag'].value_counts()\n",
    "print(f\"Number of patients in a diagnosis category: {num_patients}\")\n",
    "# get min number of patients in a diagnosis category\n",
    "min_num_patients = df_dgc['diag'].value_counts().min()\n",
    "print(f\"Min number of patients in a diagnosis category: {min_num_patients}\")\n",
    "\n",
    "# get the unique values of the diagnosis category\n",
    "unique_vals = df_dgc['diag'].unique()\n",
    "print(f\"unique values in the diagnosis category: {unique_vals}\")\n",
    "\n",
    "# get random sample of patients for each diagnosis category with min number of patients\n",
    "sampled_data = pd.DataFrame()\n",
    "for val in unique_vals:\n",
    "    sampled_data = pd.concat([sampled_data, df_dgc[df_dgc['diag'] == val].sample(min_num_patients)])\n",
    "\n",
    "\n",
    "print(sampled_data['diag'].value_counts())\n",
    "\n",
    "df_dgc = sampled_data\n",
    "\n",
    "# Change from group to category\n",
    "age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "df_dgc['age_group'] = pd.cut(df_dgc['age'], bins=age_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
    "# Plot the correlation matrix\n",
    "sns.heatmap(corr_matrix_age_diag, annot=True, cmap='coolwarm', fmt='d')\n",
    "plt.title('Correlationmatrix of Age and Diagnostic Sample Groups', fontsize=16)\n",
    "plt.xlabel('Diagnostic Group')\n",
    "plt.ylabel('Age Group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 1043.5644539016944\n",
      "P-value: 4.935370162055676e-205\n",
      "Chi-Square Statistic for SB in 60-70 vs others: 32.94855579340837\n",
      "P-value for SB in 60-70 vs others: 9.463001659861763e-09\n"
     ]
    }
   ],
   "source": [
    "# Change from group to category\n",
    "corr_matrix_age_diag= pd.crosstab(df_dgc['age_group'], df_dgc['diag'])\n",
    "\n",
    "# Chi-square test\n",
    "chi2, p, _, _ = chi2_contingency(corr_matrix_age_diag)\n",
    "\n",
    "# Difference between observed and expected frequencies\n",
    "print(f\"Chi-Square Statistic: {chi2}\")\n",
    "print(f\"P-value: {p}\")\n",
    "\n",
    "# Check if SB (Sinusbradykardie) has a significantly higher frequency in the 60-70 age group\n",
    "sb_60_70 = corr_matrix_age_diag.loc[pd.Interval(60, 70, closed='right'), 'SB']\n",
    "sb_other = corr_matrix_age_diag.drop(pd.Interval(60, 70, closed='right')).sum()['SB']\n",
    "total_60_70 = corr_matrix_age_diag.loc[pd.Interval(60, 70, closed='right')].sum()\n",
    "total_other = corr_matrix_age_diag.drop(pd.Interval(60, 70, closed='right')).sum().sum()\n",
    "\n",
    "# Frequency table for the specific Chi-Square test\n",
    "observed = [[sb_60_70, total_60_70 - sb_60_70], [sb_other, total_other - sb_other]]\n",
    "chi2_sb, p_sb = chi2_contingency(observed)[:2]\n",
    "\n",
    "\n",
    "print(f\"Chi-Square Statistic for SB in 60-70 vs others: {chi2_sb}\")\n",
    "print(f\"P-value for SB in 60-70 vs others: {p_sb}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 1043.5644539016944\n",
      "P-value: 4.935370162055676e-205\n",
      "Chi-Square Statistic for AFIB in 70-80 vs others: 120.60329273774582\n",
      "P-value for AFIB in 70-80 vs others: 4.667227334873944e-28\n"
     ]
    }
   ],
   "source": [
    "# Change from group to category\n",
    "age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "df_dgc['age_group'] = pd.cut(df_dgc['age'], bins=age_categories)\n",
    "corr_matrix_age_diag= pd.crosstab(df_dgc['age_group'], df_dgc['diag'])\n",
    "\n",
    "# Chi-square test\n",
    "chi2, p, _, _ = chi2_contingency(corr_matrix_age_diag)\n",
    "\n",
    "# Difference between observed and expected frequencies\n",
    "print(f\"Chi-Square Statistic: {chi2}\")\n",
    "print(f\"P-value: {p}\")\n",
    "\n",
    "# Check if AFIB (atrial fibrillation /atrial flutter) has a significantly higher frequency in the 70-80 age group\n",
    "afib_70_80 = corr_matrix_age_diag.loc[pd.Interval(70, 80, closed='right'), 'AFIB']\n",
    "afib_other = corr_matrix_age_diag.drop(pd.Interval(70, 80, closed='right')).sum()['AFIB']\n",
    "total_70_80 = corr_matrix_age_diag.loc[pd.Interval(70, 80, closed='right')].sum()\n",
    "total_other_70_80 = corr_matrix_age_diag.drop(pd.Interval(70, 80, closed='right')).sum().sum()\n",
    "\n",
    "# Frequency table for the specific Chi-Square test\n",
    "observed = [[afib_70_80, total_70_80 - afib_70_80], [afib_other, total_other_70_80 - afib_other]]\n",
    "chi2_afib, p_afib = chi2_contingency(observed)[:2]\n",
    "\n",
    "\n",
    "print(f\"Chi-Square Statistic for AFIB in 70-80 vs others: {chi2_afib}\")\n",
    "print(f\"P-value for AFIB in 70-80 vs others: {p_afib}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results can be interpreted as followed:\n",
    "\n",
    "- 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",
    "\n",
    "- 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The significant appearance of sinus bradycardia in the age group 60-70 could be caused by multiple factors. \n",
    "In this case, the physiological age could play a huge factor. The sinus node continuously generates electrical impulses, thus setting the normal rhythm and rate in a healthy heart. With increasing age, the sinus node becomes less responsive which leads to a slower heart rate of 60 bpm or less.\n",
    "Another reason could be increased medication, which is more likely to be the case when older. A sinus bradycardia could appear as a side effect of that medication.\n",
    "<br>(source: https://doi.org/10.1253/jcj.57.760, last visit: 10.06.2024)\n",
    "<br>(source: https://doi.org/10.7861%2Fclinmed.2022-0431, last visit: 10.06.2024)<br>\n",
    "<br>\n",
    "But what could be the reason for the more frequent appearance of the sinus bradycardia in the age group 60-70 than in other older age groups?<br>\n",
    "The lower number of sinus bradycardia cases in older age groups could be due to the increasing mortality with higher ages. People with sinus bradycardia might not reach older ages because of comorbidities and further complications.\n",
    "Besides that, older people are more likely to receive medical support such as medication and pacemakers which can prevent sinus bradycardia or at least lower its effect.\n",
    "The higher frequency of older people in the database may lead to a slight bias in the distribution. See also [Demographic Bias](#demographic-bias).<br>\n",
    "The sample size in the study conducted may also play a role in the significance of the frequency.\n",
    "\n",
    "The significant appearance of atrial fibrillation/atrial flutter in the age group 70-80 could be caused by multiple factors.\n",
    "The physiological age is the main reason. With increasing age, various age-related changes in the cardiovascular system occur. Older people are more likely to have hypertension. The increased pressure can lead to thickening of the heart walls and a change of the structure, potentially leading to AFIB. Chronic inflammation which is more prevalent in older people, can damage heart tissue and lead to atrial issues. The change of hormone levels when getting older can also have an influence on the heart function and contribute to the development of arrhythmias. Older adults are also more likely to have comorbidities like diabetes, obesity or chronic kidney disease.\n",
    "<br>(source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460064/, last visit: 28.06.2024)"
   ]
  }
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