COVID-19_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: The used input data are completely artificial and not real cases!! \n",
    "#       This is only an idea how you can interpret COVID-19 symptoms and determine a potential infection!!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Chill</th>\n",
       "      <th>Floppiness</th>\n",
       "      <th>Cold</th>\n",
       "      <th>Growing_pains</th>\n",
       "      <th>Fever</th>\n",
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       "      <th>Diarrhea</th>\n",
       "      <th>Breathlessness</th>\n",
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       "<p>500000 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "        Age  Contact  Chill  Floppiness  Cold  Growing_pains  Fever  \\\n",
       "0        97        1      1           1     0              0      0   \n",
       "1        39        1      0           1     1              1      1   \n",
       "2        32        0      1           1     1              0      1   \n",
       "3        39        0      0           0     1              1      0   \n",
       "4        84        1      1           0     1              1      1   \n",
       "...     ...      ...    ...         ...   ...            ...    ...   \n",
       "499995   25        0      1           1     0              0      0   \n",
       "499996   76        1      1           1     0              0      0   \n",
       "499997   95        0      1           0     1              1      1   \n",
       "499998   66        1      0           0     1              0      0   \n",
       "499999   85        1      1           1     0              1      1   \n",
       "\n",
       "        Continuing_cough  Diarrhea  Breathlessness  Chronic_lung_disease  \\\n",
       "0                      0         0               1                     1   \n",
       "1                      1         1               1                     0   \n",
       "2                      1         1               1                     1   \n",
       "3                      0         0               0                     0   \n",
       "4                      1         0               1                     0   \n",
       "...                  ...       ...             ...                   ...   \n",
       "499995                 1         0               0                     0   \n",
       "499996                 0         0               0                     1   \n",
       "499997                 0         0               1                     1   \n",
       "499998                 1         0               1                     0   \n",
       "499999                 1         1               1                     0   \n",
       "\n",
       "        Diabetes  Diagnosis  \n",
       "0              1          4  \n",
       "1              1          1  \n",
       "2              1          1  \n",
       "3              0          4  \n",
       "4              1          2  \n",
       "...          ...        ...  \n",
       "499995         0          3  \n",
       "499996         1          4  \n",
       "499997         0          3  \n",
       "499998         1          3  \n",
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       "\n",
       "[500000 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\user\\GitHub\\Corona\\Corona.csv\",sep=';')\n",
    "df = pd.DataFrame(data)\n",
    "df\n",
    "#df.head()\n",
    "#df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Some information about the columns:\n",
    "\n",
    "\n",
    "    - Age: Every person could be infected, but COVID-19 is more fatal to older people (> 80).\n",
    "    \n",
    "    \n",
    "    - Contact: Contact with a confirmed or suspected case (person with fever/cough; breathlessness; \n",
    "                contact with a confirmed person; travelling in regions at risk (especially Italy, Irane,\n",
    "                France, Spain, Austria, China, USA, South Korea)).\n",
    "                -> Face to face contact, physical contact (kissing, handshake), sharing a house/apartment,\n",
    "                   working situation (hospital or other medical field, university, school, nursery, home)\n",
    "\n",
    "\n",
    "    - Fever: Fever (> 38 °C) in the last 24 hours and in the last 4 days.\n",
    "    \n",
    "    \n",
    "    - Diagnosis: Assessment/Evaluation based on previous symptoms.\n",
    "                 - 1: Disease tends to COVID-19 infection (-> 100%); hospital stay is required.\n",
    "                 - 2: Disease shows typical COVID-19 symptoms; test is required. \n",
    "                 - 3: Disease shows symptoms which can cause COVID-19; quarantine/isolation at home is required\n",
    "                 - 4: Disease shows no COVID-19 symptoms, but stay at home to protect yourself and your FFF. \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of cases:                       500.000\n",
      "\n",
      "Number of cases with COVID-19 infection:     52.459    (mean age: 60)\n",
      "Number of cases with COVID-19 symptoms:      88.248    (mean age: 55)\n",
      "Number of cases in quarantine/isolation:     249.608\n",
      "Number of cases without COVID-19 symptoms:   109.685\n",
      "Number of cases without a diagnosis:         0\n",
      "\n",
      "Sum of the cases (control):                  500.000\n"
     ]
    }
   ],
   "source": [
    "tc = df.iloc[:, 12].count()\n",
    "\n",
    "d1 = df.loc[df[\"Diagnosis\"] == 1, \"Diagnosis\"].count()\n",
    "d2 = df.loc[df[\"Diagnosis\"] == 2, \"Diagnosis\"].count()\n",
    "d3 = df.loc[df[\"Diagnosis\"] == 3, \"Diagnosis\"].count()\n",
    "d4 = df.loc[df[\"Diagnosis\"] == 4, \"Diagnosis\"].count()\n",
    "d0 = df.loc[df[\"Diagnosis\"] == 0, \"Diagnosis\"].count()\n",
    "\n",
    "\n",
    "m1 = (int) (df.loc[df[\"Diagnosis\"] == 1, \"Age\"].mean())\n",
    "m2 = (int) (df.loc[df[\"Diagnosis\"] == 2, \"Age\"].mean())\n",
    "m3 = (int) (df.loc[df[\"Diagnosis\"] == 3, \"Age\"].mean())\n",
    "m4 = (int) (df.loc[df[\"Diagnosis\"] == 4, \"Age\"].mean())\n",
    "\n",
    "\n",
    "\n",
    "print(\"Total number of cases:                       \" + \"{:,}\".format(tc).replace(\",\",\".\"))\n",
    "# or: print(\"Total number of cases: \" + str(df.iloc[:, 12].count()))\n",
    "print()\n",
    "\n",
    "print(\"Number of cases with COVID-19 infection:     \" + \"{:,}\".format(d1).replace(\",\",\".\") + \"    (mean age: \" + str(m1) + \")\")\n",
    "# or: print(\"Number of cases with COVID-19 infections: \" + str(df.loc[df[\"Diagnosis\"] == 1, \"Diagnosis\"].count()))\n",
    "\n",
    "print(\"Number of cases with COVID-19 symptoms:      \" + \"{:,}\".format(d2).replace(\",\",\".\") + \"    (mean age: \" + str(m2) + \")\")\n",
    "print(\"Number of cases in quarantine/isolation:     \" + \"{:,}\".format(d3).replace(\",\",\".\"))\n",
    "print(\"Number of cases without COVID-19 symptoms:   \" + \"{:,}\".format(d4).replace(\",\",\".\"))\n",
    "print(\"Number of cases without a diagnosis:         \" + \"{:,}\".format(d0).replace(\",\",\".\"))\n",
    "print()\n",
    "\n",
    "\n",
    "\n",
    "agg = d1 + d2 + d3 + d4 + d0\n",
    "print(\"Sum of the cases (control):                  \" + \"{:,}\".format(agg).replace(\",\",\".\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Contact', 'Chill', 'Floppiness', 'Cold', 'Growing_pains',\n",
       "       'Fever', 'Continuing_cough', 'Diarrhea', 'Breathlessness',\n",
       "       'Chronic_lung_disease', 'Diabetes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:, 0:12]\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Diagnosis'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:, 12]\n",
    "y.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier:        Your disease shows typical COVID-19 symptoms; recommendation: COVID-19 test.\n",
      "AdaBoostClassifier:   Your disease shows typical COVID-19 symptoms; recommendation: COVID-19 test.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "ada = AdaBoostClassifier()\n",
    "\n",
    "\n",
    "\n",
    "for clf in [sgd, ada]:\n",
    "    model = clf.fit(X, y)\n",
    "    pred = model.predict([[67,1,0,1,1,0,1,1,0,1,0,1]])\n",
    "\n",
    "    if clf == sgd:\n",
    "        if pred == 1:\n",
    "            print(\"SGDClassifier:        Your disease tends to COVID-19 infection (-> 100%); recommendation: hospital stay.\")\n",
    "        elif pred == 2:\n",
    "            print(\"SGDClassifier:        Your disease shows typical COVID-19 symptoms; recommendation: COVID-19 test.\")\n",
    "        elif pred == 3:\n",
    "            print(\"SGDClassifier:        Your disease shows symptoms which can cause COVID-19; recommendation: quarantine/isolation at home.\")\n",
    "        elif pred == 4:\n",
    "            print(\"SGDClassifier:        Your disease shows no COVID-19 symptoms; recommendation: stay at home to protect yourself.\")\n",
    "        else:\n",
    "            print(\"No relevant case!\")\n",
    "    \n",
    "    \n",
    "    if clf == ada:\n",
    "        if pred == 1:\n",
    "            print(\"AdaBoostClassifier:   Your disease tends to COVID-19 infection (-> 100%); recommendation: hospital stay.\")\n",
    "        elif pred == 2:\n",
    "            print(\"AdaBoostClassifier:   Your disease shows typical COVID-19 symptoms; recommendation: COVID-19 test.\")\n",
    "        elif pred == 3:\n",
    "            print(\"AdaBoostClassifier:   Your disease shows symptoms which can cause COVID-19; recommendation: quarantine/isolation at home.\")\n",
    "        elif pred == 4:\n",
    "            print(\"AdaBoostClassifier:   Your disease shows no COVID-19 symptoms; recommendation: stay at home to protect yourself.\")\n",
    "        else:\n",
    "            print(\"No relevant case!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testperson 1 [56, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]\n",
      "Your disease shows symptoms which can cause COVID-19; recommendation: quarantine/isolation at home.\n",
      "\n",
      "Testperson 2 [31, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]\n",
      "Your disease shows no COVID-19 symptoms; recommendation: stay at home to protect yourself.\n",
      "\n",
      "Testperson 3 [87, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "Your disease tends to COVID-19 infection (-> 100%); recommendation: hospital stay.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "\n",
    "\n",
    "tp1 = [56,1,1,0,1,1,0,1,0,1,0,1]\n",
    "tp2 = [31,0,0,0,1,1,0,0,0,0,1,1]\n",
    "tp3 = [87,1,1,1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "\n",
    "i = 0\n",
    "for testperson in [tp1, tp2, tp3]:\n",
    "    \n",
    "    model = sgd.fit(X, y)\n",
    "    pred = model.predict([testperson])\n",
    "    \n",
    "    i += 1\n",
    "    print(\"Testperson \" + str(i), testperson)\n",
    "    \n",
    "    if pred == 1:\n",
    "        print(\"Your disease tends to COVID-19 infection (-> 100%); recommendation: hospital stay.\")\n",
    "    elif pred == 2:\n",
    "        print(\"Your disease shows typical COVID-19 symptoms; recommendation: COVID-19 test.\")\n",
    "    elif pred == 3:\n",
    "        print(\"Your disease shows symptoms which can cause COVID-19; recommendation: quarantine/isolation at home.\")\n",
    "    elif pred == 4:\n",
    "        print(\"Your disease shows no COVID-19 symptoms; recommendation: stay at home to protect yourself.\")\n",
    "    else:\n",
    "        print(\"No relevant case!\")\n",
    "\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#x = range(0,5)\n",
    "#y = [d0, d1, d2, d3, d4]\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.bar(0, d0, color=\"y\", align='center', label='(0) no relevant case')\n",
    "plt.bar(1, d1, color=\"r\", align='center', label='(1) COVID-19 infection')\n",
    "plt.bar(2, d2, color=\"g\", align='center', label='(2) COVID-19 symptoms')\n",
    "plt.bar(3, d3, color=\"b\", align='center', label='(3) quarantine/isolation')\n",
    "plt.bar(4, d4, color=\"c\", align='center', label='(4) no COVID-19 infection')\n",
    "#plt.bar(x, y, width=[0.6, 0.6, 0.6, 0.6, 0.6], color=['y','r','g','b','c'], align='center', label=\"COVID\")\n",
    "\n",
    "plt.xlabel(\"Diagnosis\", fontsize=15)\n",
    "plt.ylabel(\"Number of cases\", fontsize=15)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(loc='best', fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2a11dcdd0c8>,\n",
       "  <matplotlib.patches.Wedge at 0x2a11dcddb48>,\n",
       "  <matplotlib.patches.Wedge at 0x2a11dce4808>,\n",
       "  <matplotlib.patches.Wedge at 0x2a11dcec548>,\n",
       "  <matplotlib.patches.Wedge at 0x2a11dcf5488>],\n",
       " [Text(1.2000000000000002, 0.0, '0'),\n",
       "  Text(1.1354025226313351, 0.3884084340000878, '1'),\n",
       "  Text(0.4194687413696309, 1.1242979920882976, '2'),\n",
       "  Text(-1.177276508350349, -0.23242207917151708, '3'),\n",
       "  Text(0.9261279356832423, -0.7630773530560951, '4')],\n",
       " [Text(0.7, 0.0, '0.0%'),\n",
       "  Text(0.6623181382016121, 0.22657158650005121, '10.5%'),\n",
       "  Text(0.2446900991322847, 0.6558404953848402, '17.6%'),\n",
       "  Text(-0.6867446298710368, -0.13557954618338494, '49.9%'),\n",
       "  Text(0.5402412958152246, -0.44512845594938877, '21.9%')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "x = range(0,5)\n",
    "y = [d0, d1, d2, d3, d4]\n",
    "plt.axis(\"equal\")\n",
    "plt.pie(y, labels=x, colors=[\"y\",\"r\",\"g\",\"b\",\"c\"], shadow=True, explode=[0.1,0.1,0.1,0.1,0.1], autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}