Perisa_s_Code_PhysioNet_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'C:/Users/ashar/Downloads/Demographics Information - Human Participants (2).csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import kruskal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file into a Pandas DataFrame\n",
    "data = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-numeric values in the 'Total N' column:\n",
      "[nan]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming the column names are 'Demographic_Factor_1', 'Demographic_Factor_2', etc.\n",
    "# Replace these with your actual column names\n",
    "demographic_columns = ['Race Present', 'Ethnicity Present', 'Age Present', 'Sex Present']  # Add all your demographic columns\n",
    "\n",
    "# Sum the demographic factors for each row\n",
    "data['Sum_Demographic_Factors'] = data[demographic_columns].sum(axis=1)\n",
    "\n",
    "data['Total N'] = pd.to_numeric(data['Total N'], errors='coerce')\n",
    "\n",
    "# Check for any other non-numeric values after converting 'X' to NaN\n",
    "non_numeric_values = data[pd.to_numeric(data['Total N'], errors='coerce').isnull()]\n",
    "\n",
    "if not non_numeric_values.empty:\n",
    "    print(\"Non-numeric values in the 'Total N' column:\")\n",
    "    print(non_numeric_values['Total N'].unique())\n",
    "\n",
    "# Drop rows where 'Total N' column contains 'X' or NaN\n",
    "data = data[(data['Total N'] != 'X') & (~data['Total N'].isnull())]\n",
    "\n",
    "# Convert 'Total N' column to numeric after removing 'X' and NaN values\n",
    "data['Total N'] = pd.to_numeric(data['Total N'], errors='coerce')\n",
    "\n",
    "# Check for any remaining NaN values after dropping 'X' and NaN and converting to numeric\n",
    "if data['Total N'].isnull().values.any():\n",
    "    print(\"There are still non-numeric values in the 'Total N' column after dropping 'X' and NaN values.\")\n",
    "else:\n",
    "    data['Log_N'] = np.log(data['Total N'])\n",
    "\n",
    "# List of columns to drop\n",
    "#columns_to_drop = ['location', 'other_column']  # Add other columns you want to drop\n",
    "\n",
    "# Drop the columns not needed\n",
    "#data = data.drop(columns=columns_to_drop)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(data['Log_N'], data['Sum_Demographic_Factors'], alpha=0.7)\n",
    "plt.title('Sum of Demographic Factors vs log(N)')\n",
    "plt.xlabel('log(N)')\n",
    "plt.ylabel('Sum of Demographic Factors')\n",
    "#plt.grid(True)\n",
    "\n",
    "# Calculate correlation coefficient and p-value\n",
    "correlation_coefficient, p_value = pearsonr(data['Log_N'], data['Sum_Demographic_Factors'])\n",
    "corr_coeff_str = f'Correlation Coefficient: {correlation_coefficient:.2f}'\n",
    "p_value_str = f'p-value: {p_value:.4f}'\n",
    "plt.text(0.1, 0.9, corr_coeff_str, transform=plt.gca().transAxes)\n",
    "plt.text(0.1, 0.85, p_value_str, transform=plt.gca().transAxes)\n",
    "\n",
    "\n",
    "\n",
    "# Set y-axis ticks to include only integer values\n",
    "plt.yticks(np.arange(0, 5, 1))  # Adjust range according to your data\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert demographic variables to numeric if they're not already\n",
    "demographic_cols = ['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present']\n",
    "data[demographic_cols] = data[demographic_cols].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# Group by 'Country/Region' and sum the demographic variables\n",
    "demographic_by_country = data.groupby('Country/Region')[demographic_cols].sum()\n",
    "\n",
    "# Display the table\n",
    "demographic_by_country\n",
    "# Export to Excel\n",
    "country_demographics_excel = 'demographic_by_country.xlsx'\n",
    "demographic_by_country.to_excel(country_demographics_excel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cardiac_electrophysiology    41\n",
      "multi_parameter              31\n",
      "gait/balance/motion          18\n",
      "other                        14\n",
      "neuroelectric                13\n",
      "cardiovascular               13\n",
      "clinical                     11\n",
      "myoelectric                   9\n",
      "cardiorespiratory             4\n",
      "Name: Field, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Counting the number of studies in each field\n",
    "field_counts = data['Field'].value_counts()\n",
    "\n",
    "# Converting the counts to a DataFrame for easier plotting\n",
    "field_counts_df = field_counts.reset_index()\n",
    "field_counts_df.columns = ['Field', 'Number of Studies']\n",
    "\n",
    "# Plotting the horizontal bar graph\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='Number of Studies', y='Field', data=field_counts_df)\n",
    "\n",
    "#plt.title('Number of Studies in Each Clinical Area of Interest')\n",
    "plt.xlabel('Number of Studies')\n",
    "plt.ylabel('Field of Study')\n",
    "\n",
    "plt.show()\n",
    "print(field_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensuring the binary columns are of numeric type for correct summation\n",
    "data['Age Present'] = pd.to_numeric(data['Age Present'], errors='coerce')\n",
    "data['Race Present'] = pd.to_numeric(data['Race Present'], errors='coerce')\n",
    "data['Ethnicity Present'] = pd.to_numeric(data['Ethnicity Present'], errors='coerce')\n",
    "data['Sex Present'] = pd.to_numeric(data['Sex Present'], errors='coerce')\n",
    "\n",
    "# Grouping and summing the data\n",
    "grouped_data = data.groupby('Field').sum()[['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present']]\n",
    "\n",
    "# Melting the grouped data for plotting\n",
    "melted_data = pd.melt(grouped_data.reset_index(), id_vars=[\"Field\"],\n",
    "                      value_vars=['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present'],\n",
    "                      var_name='Demographic Variable', value_name='Frequency')\n",
    "\n",
    "\n",
    "# Define a color palette\n",
    "palette = {\"Age Present\": \"purple\", \"Race Present\": \"orange\", \"Ethnicity Present\": \"pink\", \"Sex Present\": \"blue\"}\n",
    "\n",
    "# Plotting the grouped horizontal bar chart\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='Frequency', y='Field', hue='Demographic Variable', data=melted_data, palette = palette)\n",
    "\n",
    "plt.title('Frequency of Demographic Variables by Field of Study')\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Field of Study')\n",
    "plt.legend(title='Demographic Variable')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to numeric if not already\n",
    "data['Age Present'] = pd.to_numeric(data['Age Present'], errors='coerce')\n",
    "data['Race Present'] = pd.to_numeric(data['Race Present'], errors='coerce')\n",
    "data['Ethnicity Present'] = pd.to_numeric(data['Ethnicity Present'], errors='coerce')\n",
    "data['Sex Present'] = pd.to_numeric(data['Sex Present'], errors='coerce')\n",
    "\n",
    "# Grouping and summing the data\n",
    "grouped_data = data.groupby('Field').sum()[['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present']]\n",
    "\n",
    "# Calculate total counts per field for normalization\n",
    "grouped_data['Total'] = grouped_data.sum(axis=1)\n",
    "\n",
    "# Convert counts to percentages\n",
    "for col in ['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present']:\n",
    "    grouped_data[col] = (grouped_data[col] / grouped_data['Total']) * 100\n",
    "\n",
    "# Melting the grouped data for plotting\n",
    "melted_data = pd.melt(grouped_data.reset_index(), id_vars=[\"Field\"],\n",
    "                      value_vars=['Age Present', 'Race Present', 'Ethnicity Present', 'Sex Present'],\n",
    "                      var_name='Demographic Variable', value_name='Percentage')\n",
    "\n",
    "# Define a color palette\n",
    "palette = {\"Age Present\": \"purple\", \"Race Present\": \"orange\", \"Ethnicity Present\": \"pink\", \"Sex Present\": \"blue\"}\n",
    "\n",
    "# Plotting the grouped horizontal bar chart with custom colors\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='Percentage', y='Field', hue='Demographic Variable', data=melted_data, palette=palette)\n",
    "\n",
    "plt.title('Percentage of Demographic Variables by Field of Study')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Field of Study')\n",
    "plt.legend(title='Demographic Variable')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert 'Average Age' to numeric, handling non-numeric entries\n",
    "data['Average Age'] = pd.to_numeric(data['Average Age'], errors='coerce')\n",
    "\n",
    "# Exclude rows where 'Average Age' is NaN\n",
    "data_filtered = data[data['Average Age'].notna()]\n",
    "\n",
    "# Group by 'Field' and calculate mean and standard deviation\n",
    "age_stats = data_filtered.groupby('Field')['Average Age'].agg(['mean', 'std'])\n",
    "\n",
    "# Resetting the index to make 'Field' a column again\n",
    "age_stats.reset_index(inplace=True)\n",
    "\n",
    "# Plotting the horizontal bar graph\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='mean', y='Field', data=age_stats, ci=None)\n",
    "\n",
    "# Adding error bars for standard deviation\n",
    "for i, row in age_stats.iterrows():\n",
    "    plt.errorbar(row['mean'], i, xerr=row['std'], fmt='none', color='black', capsize=5)\n",
    "\n",
    "plt.title('Average Age Distribution with Standard Deviation in Each Field of Study')\n",
    "plt.xlabel('Average Age')\n",
    "plt.ylabel('Field of Study')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis Test statistic: 22.53473831671425\n",
      "P-value: 0.004015876965967535\n",
      "There is a statistically significant difference between the groups.\n"
     ]
    }
   ],
   "source": [
    "# Convert 'Average Age' to numeric, handling non-numeric entries\n",
    "data['Average Age'] = pd.to_numeric(data['Average Age'], errors='coerce')\n",
    "\n",
    "# Grouping the data and filtering out groups with fewer than 2 non-NaN values\n",
    "grouped_data = data.groupby(\"Field\")\n",
    "age_lists = [group[\"Average Age\"].dropna().values for name, group in grouped_data if len(group[\"Average Age\"].dropna()) >= 2]\n",
    "\n",
    "# Check if there are enough groups for the test\n",
    "if len(age_lists) > 1:\n",
    "    # Perform the Kruskal-Wallis Test\n",
    "    stat, p = kruskal(*age_lists)\n",
    "    print(\"Kruskal-Wallis Test statistic:\", stat)\n",
    "    print(\"P-value:\", p)\n",
    "\n",
    "    # Interpretation of the result\n",
    "    alpha = 0.05\n",
    "    if p < alpha:\n",
    "        print(\"There is a statistically significant difference between the groups.\")\n",
    "    else:\n",
    "        print(\"There is no statistically significant difference between the groups.\")\n",
    "else:\n",
    "    print(\"Not enough groups with sufficient data for Kruskal-Wallis test.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Filter out rows where 'Male' or 'Female' column has 'X'\n",
    "data_without_X = data[(data['Male'] != 'X') & (data['Female'] != 'X')]\n",
    "# Define the list of titles to be excluded\n",
    "exclude_titles = [\n",
    "    \"Fetal ECG Synthetic Database\",\n",
    "    \"MMG Database v1.0.0\",\n",
    "    \"Non-Invasive Fetal ECG Arrhythmia Database\",\n",
    "    \"Non-Invasive Fetal ECG Database\",\n",
    "    \"Fetal PCG Database\",\n",
    "    \"Term-Preterm EHG DataSet with Tocogram\",\n",
    "    \"Term-Preterm EHG Database\",\n",
    "    \"NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research\",\n",
    "    \"Icelandic 16-electrode Electrohysterogram Database\",\n",
    "    \"Indian Institute of Science Fetal Heart Sound Database (IIScFHSDB)\",\n",
    "    \"Shiraz University Fetal Heart Sounds Database\",\n",
    "    \"Visceral adipose tissue measurements during pregnancy\",\n",
    "    \"CTU-CHB Intrapartum Cardiotocography Database\",\n",
    "    \"Abdominal and Direct Fetal ECG Database\",\n",
    "    \"OB-1 Fetal ECG Database\",\n",
    "    \"Simulated Fetal Phonocardiograms\"\n",
    "]\n",
    "# Step 2: Filter out rows with specified titles in 'Study Name'\n",
    "filtered_data = data_without_X[~data_without_X['Study Name'].isin(exclude_titles)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashar\\AppData\\Local\\Temp\\ipykernel_105168\\2638648023.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data[cols_to_convert] = filtered_data[cols_to_convert].apply(pd.to_numeric, errors='coerce', downcast='float')\n",
      "C:\\Users\\ashar\\AppData\\Local\\Temp\\ipykernel_105168\\2638648023.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data['Total'] = filtered_data['Male'] + filtered_data['Female'] + filtered_data['Unknown / Not Reported']\n",
      "C:\\Users\\ashar\\AppData\\Local\\Temp\\ipykernel_105168\\2638648023.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data['% Male'] = (filtered_data['Male'] / filtered_data['Total']) * 100\n",
      "C:\\Users\\ashar\\AppData\\Local\\Temp\\ipykernel_105168\\2638648023.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data['% Female'] = (filtered_data['Female'] / filtered_data['Total']) * 100\n",
      "C:\\Users\\ashar\\AppData\\Local\\Temp\\ipykernel_105168\\2638648023.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data['% Difference'] = filtered_data['% Male'] - filtered_data['% Female']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert necessary columns to numeric\n",
    "cols_to_convert = ['Male', 'Female', 'Unknown / Not Reported']\n",
    "filtered_data[cols_to_convert] = filtered_data[cols_to_convert].apply(pd.to_numeric, errors='coerce', downcast='float')\n",
    "\n",
    "# Calculate the total for the denominator\n",
    "filtered_data['Total'] = filtered_data['Male'] + filtered_data['Female'] + filtered_data['Unknown / Not Reported']\n",
    "\n",
    "# Calculate percentage of Males and Females\n",
    "filtered_data['% Male'] = (filtered_data['Male'] / filtered_data['Total']) * 100\n",
    "filtered_data['% Female'] = (filtered_data['Female'] / filtered_data['Total']) * 100\n",
    "\n",
    "# Calculate the percentage difference\n",
    "filtered_data['% Difference'] = filtered_data['% Male'] - filtered_data['% Female']\n",
    "\n",
    "# Group by 'Field' and calculate mean and standard deviation of the percentage difference\n",
    "field_stats = filtered_data.groupby('Field')['% Difference'].agg(['mean', 'std'])\n",
    "\n",
    "# Reset index for easier plotting\n",
    "field_stats.reset_index(inplace=True)\n",
    "\n",
    "# Plotting the horizontal bar graph\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='mean', y='Field', data=field_stats, ci=None)\n",
    "\n",
    "# Adding error bars for standard deviation\n",
    "for i, row in field_stats.iterrows():\n",
    "    plt.errorbar(row['mean'], i, xerr=row['std'], fmt='none', color='black', capsize=5)\n",
    "\n",
    "plt.title('Average Difference of Male and Female Percentages by Field with Standard Deviation')\n",
    "plt.xlabel('Average Percentage Difference')\n",
    "plt.ylabel('Field of Study')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis Test statistic: 9.444596307723433\n",
      "P-value: 0.30618837343082195\n",
      "There is no statistically significant difference between the groups.\n"
     ]
    }
   ],
   "source": [
    "# Creating a list of percentage differences for each field\n",
    "percentage_diff_lists = [group['% Difference'].dropna().values for name, group in filtered_data.groupby('Field')]\n",
    "\n",
    "# Perform the Kruskal-Wallis Test\n",
    "# Ensure there are at least two groups with more than one observation each\n",
    "if len(percentage_diff_lists) > 1 and all(len(lst) > 1 for lst in percentage_diff_lists):\n",
    "    stat, p = kruskal(*percentage_diff_lists)\n",
    "    print(\"Kruskal-Wallis Test statistic:\", stat)\n",
    "    print(\"P-value:\", p)\n",
    "\n",
    "    # Interpret the result\n",
    "    alpha = 0.05\n",
    "    if p < alpha:\n",
    "        print(\"There is a statistically significant difference between the groups.\")\n",
    "    else:\n",
    "        print(\"There is no statistically significant difference between the groups.\")\n",
    "else:\n",
    "    print(\"Not enough data for Kruskal-Wallis test.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}