oasis 데이터 전처리.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oasis 정형 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "      <th>Visit</th>\n",
       "      <th>MR Delay</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "      <td>2</td>\n",
       "      <td>457</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Demented</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Demented</td>\n",
       "      <td>2</td>\n",
       "      <td>560</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Demented</td>\n",
       "      <td>3</td>\n",
       "      <td>1895</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  EDUC  \\\n",
       "0  OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87    14   \n",
       "1  OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88    14   \n",
       "2  OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75    12   \n",
       "3  OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76    12   \n",
       "4  OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80    12   \n",
       "\n",
       "   SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
       "0  2.0  27.0  0.0  1987  0.696  0.883  \n",
       "1  2.0  30.0  0.0  2004  0.681  0.876  \n",
       "2  NaN  23.0  0.5  1678  0.736  1.046  \n",
       "3  NaN  28.0  0.5  1738  0.713  1.010  \n",
       "4  NaN  22.0  0.5  1698  0.701  1.034  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/yonghyeun/Dementia_Analysis/main/data/oasis/oasis_demographic.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    206\n",
       "0.5    123\n",
       "1.0     41\n",
       "2.0      3\n",
       "Name: CDR, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CDR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5    19\n",
       "0.0    18\n",
       "Name: CDR, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = (df['Group'] == 'Converted')\n",
    "\n",
    "df[cond]['CDR'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 33,  34,  35,  36,  37,  38,  57,  58,  59,  81,  82,  83, 114,\n",
       "            115, 194, 195, 218, 219, 220, 245, 246, 261, 262, 263, 264, 265,\n",
       "            271, 272, 273, 274, 295, 296, 297, 298, 346, 347, 348],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_idx = df[df['Group'] == 'Converted'].index\n",
    "\n",
    "converted_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (df['Group'] == 'Converted') & (df['CDR'] == 0.0)\n",
    "\n",
    "df.loc[cond, 'Group'] = 'Nondemented' # converted 인데 CDR 이 0.0 인 경우엔 Non demented 로 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (df['Group'] == 'Converted') & (df['CDR'] == 0.5) \n",
    "\n",
    "df.loc[cond, 'Group'] = 'Mild demented' # converted CDR 이 0.5 인 경우엔 mild demented 로 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (df['Group'] == 'Demented') & (df['CDR'] == 0.5) \n",
    "\n",
    "df.loc[cond, 'Group'] = 'Mild demented'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (df['Group'] == 'Demented') & (df['CDR'] >= 1) \n",
    "\n",
    "df.loc[cond, 'Group'] = 'Moderate demented'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (df['Group'] == 'Nondemented') & (df['CDR'] > 0) # CDR 이 0.5 이상이면서 Nondemented 인 경우엔 이상치러 초리하자\n",
    "\n",
    "df = df.drop(index = df[cond].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>MRI ID</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR1</td>\n",
       "      <td>Nondemented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAS2_0001</td>\n",
       "      <td>OAS2_0001_MR2</td>\n",
       "      <td>Nondemented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR1</td>\n",
       "      <td>Mild demented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR2</td>\n",
       "      <td>Mild demented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OAS2_0002</td>\n",
       "      <td>OAS2_0002_MR3</td>\n",
       "      <td>Mild demented</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Subject ID         MRI ID          Group\n",
       "0  OAS2_0001  OAS2_0001_MR1    Nondemented\n",
       "1  OAS2_0001  OAS2_0001_MR2    Nondemented\n",
       "2  OAS2_0002  OAS2_0002_MR1  Mild demented\n",
       "3  OAS2_0002  OAS2_0002_MR2  Mild demented\n",
       "4  OAS2_0002  OAS2_0002_MR3  Mild demented"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mri_label = df[['Subject ID','MRI ID','Group']]\n",
    "mri_label.to_csv('MRI_LABEL.csv',index = False) # MRI 용 라벨 \n",
    "\n",
    "mri_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "converted_df = df.iloc[converted_idx]\n",
    "\n",
    "id = converted_df['Subject ID'].unique()\n",
    "\n",
    "converted_df_list = []\n",
    "\n",
    "for _ in id:\n",
    "    \n",
    "    cond =  converted_df['Subject ID'] == _\n",
    "    \n",
    "    group = converted_df[cond]['Group'].unique()\n",
    "    \n",
    "    if len(group) >= 2:\n",
    "        \n",
    "        for g in group:\n",
    "            \n",
    "            cond = (converted_df['Subject ID'] == _) & (converted_df['Group'] ==g)\n",
    "            \n",
    "            cond_df = converted_df[cond]\n",
    "            \n",
    "            visits = cond_df['Visit'].values\n",
    "                        \n",
    "            post_visit = np.min(visits)\n",
    "            \n",
    "            converted_df_list.append(cond_df[cond_df['Visit'] == post_visit])\n",
    "            \n",
    "    else:\n",
    "        \n",
    "        cond_df = converted_df[cond]\n",
    "        \n",
    "        visits = cond_df['Visit'].values\n",
    "                    \n",
    "        post_visit = np.min(visits)\n",
    "        \n",
    "        converted_df_list.append(cond_df[cond_df['Visit'] == post_visit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_converted = df.drop(converted_idx,axis = 0)\n",
    "\n",
    "id = non_converted['Subject ID'].unique()\n",
    "\n",
    "non_converted_list = []\n",
    "\n",
    "for _ in id:\n",
    "    \n",
    "    cond = non_converted['Subject ID'] == _\n",
    "    \n",
    "    cond_df = non_converted[cond]\n",
    "    \n",
    "    visits = cond_df['Visit'].values\n",
    "    \n",
    "    post_visit = np.min(visits)\n",
    "    \n",
    "    non_converted_list.append(cond_df[cond_df['Visit'] == post_visit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "converted_df = pd.concat(converted_df_list)\n",
    "non_converted_df = pd.concat(non_converted_list)\n",
    "\n",
    "df = pd.concat([converted_df,non_converted_df])\n",
    "\n",
    "df = df.sort_values(by = 'Subject ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('OASIS.csv',index = False)"
   ]
  }
 ],
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