predicting_chronic_kidney_disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Chronic Kidney Disease"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“1012 parsing failures.\n",
      "row col           expected actual                         file\n",
      "  1 rbc value in level set      ? 'chronic_kidney_disease.csv'\n",
      "  1 sod a number                ? 'chronic_kidney_disease.csv'\n",
      "  1 pot a number                ? 'chronic_kidney_disease.csv'\n",
      "  2 rbc value in level set      ? 'chronic_kidney_disease.csv'\n",
      "  2 bgr a number                ? 'chronic_kidney_disease.csv'\n",
      "... ... .................. ...... ............................\n",
      "See problems(...) for more details.\n",
      "”Warning message in RNGkind(sample.kind = \"Rounding\"):\n",
      "“non-uniform 'Rounding' sampler used”"
     ]
    }
   ],
   "source": [
    "library(readr)\n",
    "\n",
    "# import dataset, coercing attributes to the appriopriate type\n",
    "chronic_kidney_disease <- read_csv(\"chronic_kidney_disease.csv\", \n",
    "col_types = cols(age = col_number(), \n",
    "    al = col_factor(levels = c(\"0\", \"1\", \n",
    "        \"2\", \"3\", \"4\", \"5\")), ane = col_factor(levels = c(\"yes\", \n",
    "        \"no\")), appet = col_factor(levels = c(\"good\", \n",
    "        \"poor\")), ba = col_factor(levels = c(\"present\", \n",
    "        \"notpresent\")), bgr = col_number(), \n",
    "    bp = col_number(), bu = col_number(), \n",
    "    cad = col_factor(levels = c(\"yes\", \n",
    "        \"no\")), class = col_factor(levels = c(\"ckd\", \n",
    "        \"notckd\")), dm = col_factor(levels = c(\"yes\", \n",
    "        \"no\")), hemo = col_number(), \n",
    "    htn = col_factor(levels = c(\"yes\", \n",
    "        \"no\")), pc = col_factor(levels = c(\"normal\",\n",
    "        \"abnormal\")), pcc = col_factor(levels = c(\"present\",\n",
    "        \"notpresent\")), pcv = col_number(),\n",
    "    pe = col_factor(levels = c(\"yes\",\n",
    "        \"no\")), pot = col_number(), rbc = col_factor(levels = c(\"normal\",\n",
    "        \"abnormal\")), rbcc = col_number(),\n",
    "    sc = col_number(), sg = col_factor(levels = c(\"1.005\",\n",
    "        \"1.010\", \"1.015\", \"1.020\", \"1.025\")),\n",
    "    sod = col_number(), su = col_factor(levels = c(\"0\",\n",
    "        \"1\", \"2\", \"3\", \"4\", \"5\")), wbcc = col_number()))\n",
    "\n",
    "# drop attributes with numerous missing values\n",
    "data <- chronic_kidney_disease[1:nrow(chronic_kidney_disease), -c(3, 4, 5, 6, 7, 10, 13, 14, 15, 16, 17, 18)]\n",
    "\n",
    "# use only rows containing no missing values\n",
    "data <- data[complete.cases(data),]\n",
    "\n",
    "# take random sample of 50% of observations and split into training and testing sets\n",
    "RNGkind(sample.kind = \"Rounding\")\n",
    "set.seed(1)\n",
    "train <- sample(1:nrow(data), nrow(data)/2)\n",
    "data.test <- data[-train,]\n",
    "data.train <- data[train,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          \n",
       "train.pred ckd notckd\n",
       "    ckd     98      0\n",
       "    notckd   9     70"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "         \n",
       "test.pred ckd notckd\n",
       "   ckd     95      0\n",
       "   notckd  18     65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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wjJGELyiZCMISSfCMkYQvKJkIwhJJ8I\nyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSf\nCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEk\nnwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwh\nJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCM\nISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQ\njCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJ\nkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELy\niZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC\n8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkY\nQvKJkIwhJJ8IyRhC8omQjCEknwjJGELyiZCMISSfCMkYQvKJkIwhJJ8IyRhC8omQjCEknwjJ\nGELyiZDKqlVaXp7Wa++jHk3PPnFi/7rG2a9mr5EakKdlWjS5/aAuG3JXun9yY/2AI68t7zZj\nVxBSWbXKhB6TLj2zS83d4dxPq/t9Z9HM+q5/Sk2fLMujNcIB+UrNm+HkI9KSu9KtMmDuZfOO\nGhLb5uMLEVJZtYosSl08W9f3kyB4pW7q5tTM891GBLkhrZDLw8mZ8kTuSuNr3gmXb4hp47ED\nhFRWrdJzY3jZIncEwQL54/uh6bIu9aht2QvRGuGAbO69/9YgeK/+kCB3pfH178W58dgBQiqr\nVpkUXf48vGMaIxlrOtaIBuR8uScIrpH/CnJXukH6zr9rfSwbjp0gpLJqlVnR5b0yLwia5Xer\n0j7qWCMakNer/i3YPqjrx0HeSreNqxYZ93gsm44dIqSyyrlHGilPdl4jPSBTqtc9JGeGU3kr\nffzgvLp93iz5dmJ3EVJZ5TxHmivnd14jPSB3y6UnpQvqvNJiuaXEW4ndR0hllfOq3Yu1dY+E\nCzemosp5sSEIth6wb92h0Xz2Sg99Hk6dJXeWfbuxM4RUVu1/R/ptOPeL2qqpixdO6zo8yH35\nO+WHIjelr5K1Up/+MxcuniTDN8ex6dghQiqr6MyGnl0mPNI2e+qX6nsNn7c66BTS27LPxiB/\npeXHD9y7x4glH5Z/u7EzhFRWrdHJCjvSNiD3hy/rwQ9CKqtdDmmivFj6rYEeQiqrXQvpmSun\nycll2R5oIaSy2rWQbpCep/BEyBdCMob3I/lESMYQkk+EZAwh+URIxhCST4kJ6dzeyVBTE/cW\nKDk3xoMhBokJafBeYxOhuTnuLdCxV4Xds15/veI/FmtIFTZw1jEeRSAkZDAeRSAkZDAeRSAk\nZDAeRSAkZFTQeBQ676tPUzH/IiEho4LGg5BQOhU0HoSE0qmg8SAklE7yx2PNjMb6AVN+lQlp\n29lywpbUxbJhDQd8fyMhQUfix2N5dcOMi84ceUxbSFtOkvnbUou/K00XXDhwQk9Cgoqkj8fz\nNb1fCS/fSof0wYSqa8LZ1TJyUxB8MloICSqSPh7z5CeZyVRI64bV3RZNt0j0hT33ERJ0JH08\nRsnrmclWGdvY/eH09Aj5ILzYSEjQkfTxaJb2j+lsld4yqu1Lq5pq05ddCQkqkj4eOfdILVfJ\n6Pejae6RoCvp45H7HClYJge/G07zHAm6kj4eL9T0jr4fu+1Vu2B51dDwe0kfTb9qdyghQUfi\nx+PG6oYZF88dMzHzB9kV1YP+mrqYI838HQl6kj8ejx/fr65x6l3tpwjdXtv0RhBs+/GQ+v05\nswFaGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8i\nEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8i\nEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8i\nEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEBIyGI8iEFJytX0t3S5jPIpASMm1hyFt\n/c+vf7lLr1E/+CC99C//3r9h8CWfZK03VCL9lTYzGQgpufYwpC0y4Ohvfb2f7LcunHuxZ9W0\ncw+VsZs71hta3RI6R29LE4CQkmt3Qtr0XPt4bI8C+nS2zAkvD5cVQbDtFLmiY92hDZpbmRCE\nlFypkF6e1mvvox4NZ+6Vy6OFPQZ1XvHz+2d3nZ0/Hn+Qiamfz8iocObt6gO2t/+GkAogpORq\nlQk9Jl16Zpeau4MdhbRmQT+pmfxA/nicLeemfl4nF0Vzo+S19t8MrbvyjPk3fVDCLXeIkJKr\nVWRR6uLZur6ffGFIr102UOSI698NpzvG49y5swbLiL+lps6SW6IlM+V37ddJv9jQ7fZSb78r\nhJRcrdJzY3jZIncUDmn9ssNEhi9Z2zbbMR5dU6F8PYprhtwdLfmu/Hf71a5atX7zSwuqax4r\n7eb7QkjJ1SqTosufh3dMhUJqll6LnuuYzRqP7evvaBrwTNAR0hxZmfevXyLHlmKjvSKk5GqV\nWdHlvTKvcEjTREZes659Nnc8XpIRQeGHdmlrpY/+JvtFSMmVc490n1wSTn9Wm/0c6Y0rhknV\n+BveTc/ljUejbOh4sWF01osNaRukW0m22ilCSq6c50hPyGnh9J8l71W7p89rlJopN38Y5I/H\nP2rkH+HL36PDmXeq99+ee73gbhlZqg33iJCSK+dVu4179VgfBB8dmR9SEGxbdXp3aVjSPh5r\noqdNfz9ejg4vD5dbU6vMTv9BdsWy94LgyefDyaf2k6Xl2hEPCCm52v+O9Ntw7jzpf9Zpjd/s\nXuAPssGWO6ef0T4eV8vAr31rQhdpfDWce7FH9fTvj5EjolOEBslT4aO9QZNPHF0lx31Wrh3x\ngJCSKzqzoWeXCY9Ec1svb6pruvTTQmc2RL9uH49XLhjTt6bH4T/YkP7FX07pVz/w4k3RdBTS\ns3MO6V3bd8rK/Md6lY2QkMF4FIGQkMF4FIGQkMF4FIGQkMF4FIGQkMF4FCHOkIYMifHGHZhY\nVV4iZb7BiXH/F1YUZ0jPPhvjjTswuOd5ZTV7dnlvr2eS7gHjDAk7lvSHWonaP0KyK1EHWgGJ\n2j9CsitRB1oBido/QrIrUQdaAYnav5hCKvRJUX2aYtgQyxJ1oBWQqP0jJLsSdaAVkKj9IyS7\nEnWgFZCo/SMkuxJ1oBWQqP0rb0hrZjTWD5jyq0xI286WE7akLpYNazjg+xsJKU+iDrQCErV/\nZQ1peXXDjIvOHHlMW0hbTpL524LwM9OaLrhw4ISehJQrUQdaAYnav3KG9HxN71fCy7fSIX0w\noeqacHa1jNwUBJ+MFkLKlagDrYBE7V85Q5onP8lMpkJaN6zutmi6Jf0ZhPcRUp5EHWgFJGr/\nyhnSKHk9M9kqYxu7P5yeHiHR57FvJKQ8iTrQCkjU/pUzpGZp/7aqVukto9o+XaOpNn3ZlZBy\nJepAKyBR+xfbPVLLVTL6/Wiae6QvkKgDrYBE7V98z5GCZXJw9Fm5PEf6Aok60ApI1P6VM6QX\nanpHnznY9qpdsLxq6Dup2UfTr9odSkh5EnWgFZCo/Svr35FurG6YcfHcMRMzf5BdUT3or0H4\nlSHN/B2pgEQdaAUkav/Ke2bD48f3q2ucelf7KUK31za9EQTbfjykfn/ObOgkUQdaAYnaP96P\nZFeiDrQCErV/hGRXog60AhK1f4RkV6IOtAIStX+EZFf6QPvNgvFd5eScX+QsSn/HuPQv99YV\nj5BQFukDbYx0H5IXUs6iodUtoXPKvXXFIySU2KboS/PSB9rq17ffmxdSzqKhDeXdNj2EhD12\n/+TG+gFHXhtOtr/LMc/n98/uOjucaD/Q8kPKWURIJhBSWd0qA+ZeNu+o8EPPO97lmGPNgn5S\nM/mBcHIXQ6q78oz5N31Qum0uGULCnhpfE54UFWzIeZdjh9cuGyhyxPXvpud2MaTotYZut5dq\nk0uHkLCnxte/1zaVdQZvm/XLDhMZvmRt+4JdC+mqVes3v7SguuYx9Y0tNULCnrpB+s6/a304\nlfWekjbN0mvRc9kLdi2ktEvkWMXNLA9Cwh67bVy1yLjHc97l2GaayMhr1mUt2J2Q1kof3Q0t\nA0JCET5+cF7dPm8WuEcK3rhimFSNv+HdzPzuhLRBumlvaMkREoqzWG4p8Bwp9PR5jVIz5eYP\no5ndCeluGam9lSVHSNhTD30e/jxL7sx+l2OObatO7y4NS8LJAiGtWPZe3qInnw9/PrWfLC3h\nZpcGIWFP9ek/c+HiSTJ8c/a7HPNtuXP6GeFl27l2LS1fk+aWlgvCmUHyVN6i62TQ5BNHV8lx\nn5VvL5QQEgp6ePq+9Qcctzo1tfGOWcO6dD/yZ9uyftt2cmnN3j1GLIkeurW/y7GAreGP9IF2\nSfp66Tfip0PKXvTsnEN61/adsnJ7CXesRAgJhSyWhmNmTupzSWpymdSPnXF0rRyXVdLun1ya\nqAOtgETtHyEVY1PW331ulnFvpy62/T3149c3fpT6+fK+knW+we6fE5eoA62ARO0fIaX9dHrz\nXj2OvjOa3rp0aMMB52Y+Q+KJE/vXNc5+tfNV2k8ujXw6oOu7+WtcLXM7ZggpX6L2j5DSqo74\nzuIz9pUfhdNnSPMFCwe1farRT6v7fWfRzPquf8q7QtbJpZEHZPaWOy698uHs5yo3StYDud0/\nuTRRB1oBido/Qkp7M/zxyWFdNgTBw9Hn7G0+LHou/0rd1PAMhOe7jcheO/fk0sgP5ZyDwhcA\nxnUs3D5WVnWssPsnlybqQCsgUftHSBnbP3p3/ZVyTxCcJv8bzj8YhbRA/vh+aLq0n7zTfnLp\nCXVZqkWqautqq6Qqa1F11go1tXV1tam1aut2VVXVztcpygmx/ceOHHRQvLevipDSnj1un+ge\n48YgOCT9WeSbopDGSMaazKrtJ5cO3ufbHYZI1XGpi1ld5Ni2JV+R3id/O9/Bsl+nZV9k0qRd\nXnWP7BPzPcLvfx/v7asipMgzXXr9xy9//8AFsizv2zGa5Xer0j7KrNt+cmnOQ5OL5eDoskWW\npxcslTEbOt+QpZNLE/XQKm6EFJmdfjazJAwp5x5ppDzZaeXMyaU5B+KtMiG6PDf8J1Iul3Ef\ndbqmrZNLCUkRIUWOlI3hxVfDCk4NnyhlniPNlfMLrR+dXLr3vh92LHm7qu9n6X8ieoZ1nkzc\nWOiKlk4uJSRFhBQ5VX6b+vlLCUNaJaM/CYIth0chvVhb90i4wsY78q6xbdXp1W0nl6adKJcH\n4cmkfTelfjlHpna83Sg609TgyaWEpIiQIn+uaTjtsmk1M6LHZS1y4IULB0/oeWD4m1/UVk1d\nvHBa1+GdrzRoQPrk0rR3mmXc/G9W14V3SNdK9SnRCUFRNdEJcgZPLrUUUtu3KuRw9a0KhJS2\n+qju3b/6yMoopK3XHlS//zkbatMPwlpP/VJ9r+HzVne+TupA3Jo1+/7ZTXV9TghPKg0WZV7q\nmxrORSEZPLk0tpBe7/z+KkJKrOdk1k7WsPR/9D1BSIoIqbPoq20/mSKdP7sxFyHtIUKqDPOH\nz7/ie01y7M4ehRHSjqXiePOUPnsddl969n8m7LPXwVf/MzyXN7IyyPq02XRI286WE7akLpYN\nazjA2TfPEVJn907pX7/36Ot2+qoAIe1Yq3y1/6HfO6mmOvrIvYWy7/cu/Bc55rPgpaUyduXK\nlWuzP202CmnLSTI/fAfXd6XJ3XehEtKeI6QdaxW5NHWvvlKmpWYekwP/FgSfHytXdjy0y/q0\n2TCkDyZUXRPOrk5/O/doV9/OTUh7jpB2rFW+HH7Wy/Ye4Xc3nS4rwmWvVB3YEVLWJymlQlo3\nrO62aLpF7g4v7iMkJ7asLU5zc5H/wJZ497/0IU2PLofXp36MaDt9fj/5sD2krM/2a5Wxjd0f\nTk+PSJ+jtZGQnBgpMYv5bKHhBf7IrCnzStzImtSPJvlnNDMmFVQmpKxPm22V3jKq7STfnLOG\nvajkkAbv/4ei3HNPcdffP+aHhu+/X9p/Pyeknd4jtVwlo9MbxD2SM3E/x4n79kstJ6QWuSWc\nfi18jrRWvhX9Ivc5UrBMDo7eXsxzJGfiPpDjvv1SywnpjzLo70Hw+TdkSRB8LIdHv8j6tNlo\n3eVVQ8Ovj3o0/ardoYTkRNwHcty3X2o5IQXnS//5C/9Vjvo0NX2EzPrBFS9mf9pset0V1YP+\nmrqYI838HcmRuA/kuG+/1HJDCm4b361h+JLolcrXv9mrKjqzof3TZtvWvb226Y0g2PbjIfX7\nc2aDG3EfyHHffjb357rFjZAq8PaTeNJo3AipAm+fkPQRUgXePiHpI6Tk3n5FvY0hboSU3Nuv\nqLcxxK2SQxoyJNm3X1FvY4hbJYfU2prs26+otzHErZJDSrqKehtD3AgpuSrqbQxxI6Tkqqi3\nMcSNkJKrot7GEDdCSov7D5KluP2KehtD3CoypLj/sl+m26+otzHEjZDSkh9Swt/GEDdCSkti\nSCijJIcU97lmcd8+yijZIcV7rlnct48ySnZI8Z5rFvfto4ySHVK855rFffsoo2SHFO+5ZnHf\nPsoo2SHFe65Z3LePMqqYkGI41yzu20cZVUxIMZxrFvfto4wqJqQYzjWL+/ZRRhUTUgznmsV9\n+yijygmp/OeaxX37KKMkhwSUDSEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBI\ngAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBI\ngAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAgTv3jkAAAEwSURBVBQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQo\nICRAASEBCggJUEBIgAJCAhT8P165L4d1mR4HAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build decision tree\n",
    "library(tree)\n",
    "data.train.tree = tree(class ~ ., data=data.train)\n",
    "plot(data.train.tree)\n",
    "text(data.train.tree, pretty=0)\n",
    "train.pred = predict(data.train.tree, newdata=data.train, type=\"class\")\n",
    "test.pred = predict(data.train.tree, newdata=data.test, type=\"class\")\n",
    "\n",
    "# training accuracy\n",
    "table(train.pred, data.train$class)\n",
    "# testing accuracy\n",
    "table(test.pred, data.test$class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop attributes with numerous missing values\n",
    "data <- chronic_kidney_disease[1:nrow(chronic_kidney_disease), -c(3, 4, 5, 6, 7, 10, 13, 14, 15, 16, 17, 18)]\n",
    "\n",
    "# use only rows containing no missing values\n",
    "data <- data[complete.cases(data),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes ‘tbl_df’, ‘tbl’ and 'data.frame':\t355 obs. of  13 variables:\n",
      " $ age  : num  48 7 62 48 51 60 68 52 53 50 ...\n",
      " $ bp   : num  80 50 80 70 80 90 70 100 90 60 ...\n",
      " $ pcc  : Factor w/ 2 levels \"present\",\"notpresent\": 2 2 2 1 2 2 2 1 1 1 ...\n",
      " $ ba   : Factor w/ 2 levels \"present\",\"notpresent\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ bu   : num  36 18 53 56 26 25 54 60 107 55 ...\n",
      " $ sc   : num  1.2 0.8 1.8 3.8 1.4 1.1 24 1.9 7.2 4 ...\n",
      " $ htn  : Factor w/ 2 levels \"yes\",\"no\": 1 2 2 1 2 1 2 1 1 1 ...\n",
      " $ dm   : Factor w/ 2 levels \"yes\",\"no\": 1 2 1 2 2 1 2 1 1 1 ...\n",
      " $ cad  : Factor w/ 2 levels \"yes\",\"no\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ appet: Factor w/ 2 levels \"good\",\"poor\": 1 1 2 2 1 1 1 1 2 1 ...\n",
      " $ pe   : Factor w/ 2 levels \"yes\",\"no\": 2 2 2 1 2 1 2 2 2 2 ...\n",
      " $ ane  : Factor w/ 2 levels \"yes\",\"no\": 2 2 1 1 2 2 2 1 1 1 ...\n",
      " $ class: Factor w/ 2 levels \"ckd\",\"notckd\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " - attr(*, \"problems\")=Classes ‘tbl_df’, ‘tbl’ and 'data.frame':\t1012 obs. of  5 variables:\n",
      "  ..$ row     : int  1 1 1 2 2 2 2 2 3 3 ...\n",
      "  ..$ col     : chr  \"rbc\" \"sod\" \"pot\" \"rbc\" ...\n",
      "  ..$ expected: chr  \"value in level set\" \"a number\" \"a number\" \"value in level set\" ...\n",
      "  ..$ actual  : chr  \"?\" \"?\" \"?\" \"?\" ...\n",
      "  ..$ file    : chr  \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" ...\n",
      "Classes ‘tbl_df’, ‘tbl’ and 'data.frame':\t355 obs. of  13 variables:\n",
      " $ age  : num  47 6 61 47 50 59 67 51 52 49 ...\n",
      " $ bp   : num  79 49 79 69 79 89 69 99 89 59 ...\n",
      " $ pcc  : num [1:355, 1:2] 0 0 0 1 0 0 0 1 1 1 ...\n",
      " $ ba   : num [1:355, 1:2] 0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ bu   : num  35 17 52 55 25 24 53 59 106 54 ...\n",
      " $ sc   : num  0.2 -0.2 0.8 2.8 0.4 0.1 23 0.9 6.2 3 ...\n",
      " $ htn  : num [1:355, 1:2] 1 0 0 1 0 1 0 1 1 1 ...\n",
      " $ dm   : num [1:355, 1:2] 1 0 1 0 0 1 0 1 1 1 ...\n",
      " $ cad  : num [1:355, 1:2] 0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ appet: num [1:355, 1:2] 1 1 0 0 1 1 1 1 0 1 ...\n",
      " $ pe   : num [1:355, 1:2] 0 0 0 1 0 1 0 0 0 0 ...\n",
      " $ ane  : num [1:355, 1:2] 0 0 1 1 0 0 0 1 1 1 ...\n",
      " $ class: num [1:355, 1:2] 1 1 1 1 1 1 1 1 1 1 ...\n",
      " - attr(*, \"problems\")=Classes ‘tbl_df’, ‘tbl’ and 'data.frame':\t1012 obs. of  5 variables:\n",
      "  ..$ row     : int  1 1 1 2 2 2 2 2 3 3 ...\n",
      "  ..$ col     : chr  \"rbc\" \"sod\" \"pot\" \"rbc\" ...\n",
      "  ..$ expected: chr  \"value in level set\" \"a number\" \"a number\" \"value in level set\" ...\n",
      "  ..$ actual  : chr  \"?\" \"?\" \"?\" \"?\" ...\n",
      "  ..$ file    : chr  \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" \"'chronic_kidney_disease.csv'\" ...\n"
     ]
    }
   ],
   "source": [
    "library(keras)\n",
    "str(data)\n",
    "data$age <- as.numeric(data$age)-1\n",
    "data$bp <- as.numeric(data$bp)-1\n",
    "data$bu <- as.numeric(data$bu)-1\n",
    "data$sc <- as.numeric(data$sc)-1\n",
    "\n",
    "data$pcc <- to_categorical(as.numeric(data$pcc)-1)\n",
    "data$ba <- to_categorical(as.numeric(data$ba)-1)\n",
    "data$htn <- to_categorical(as.numeric(data$htn)-1)\n",
    "data$dm <- to_categorical(as.numeric(data$dm)-1)\n",
    "data$cad <- to_categorical(as.numeric(data$cad)-1)\n",
    "data$appet <- to_categorical(as.numeric(data$appet)-1)\n",
    "data$pe <- to_categorical(as.numeric(data$pe)-1)\n",
    "data$ane <- to_categorical(as.numeric(data$ane)-1)\n",
    "data$class <- to_categorical(as.numeric(data$class)-1)\n",
    "str(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "n <- nrow(data)\n",
    "p <- ncol(data)-1\n",
    "\n",
    "set.seed(1)\n",
    "train <- sample(n, 0.8*n)\n",
    "train_data <- data[train, 1:p] %>% as.matrix()\n",
    "train_labels <- data[train, p+1, drop = FALSE] %>% as.matrix()\n",
    "test_data <- data[-train, 1:p] %>% as.matrix()\n",
    "test_labels <- data[-train, p+1] %>% as.matrix()\n",
    "\n",
    "#train_data <- data[ , 1:12] %>% as.matrix()\n",
    "#train_labels <- data[ , 13, drop = FALSE] %>% as.matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>age</dt>\n",
       "\t\t<dd>52.2887323943662</dd>\n",
       "\t<dt>bp</dt>\n",
       "\t\t<dd>75.7957746478873</dd>\n",
       "\t<dt>pcc.1</dt>\n",
       "\t\t<dd>0.112676056338028</dd>\n",
       "\t<dt>pcc.2</dt>\n",
       "\t\t<dd>0.887323943661972</dd>\n",
       "\t<dt>ba.1</dt>\n",
       "\t\t<dd>0.0633802816901408</dd>\n",
       "\t<dt>ba.2</dt>\n",
       "\t\t<dd>0.936619718309859</dd>\n",
       "\t<dt>bu</dt>\n",
       "\t\t<dd>58.2753521126761</dd>\n",
       "\t<dt>sc</dt>\n",
       "\t\t<dd>2.08890845070423</dd>\n",
       "\t<dt>htn.1</dt>\n",
       "\t\t<dd>0.411971830985915</dd>\n",
       "\t<dt>htn.2</dt>\n",
       "\t\t<dd>0.588028169014085</dd>\n",
       "\t<dt>dm.1</dt>\n",
       "\t\t<dd>0.380281690140845</dd>\n",
       "\t<dt>dm.2</dt>\n",
       "\t\t<dd>0.619718309859155</dd>\n",
       "\t<dt>cad.1</dt>\n",
       "\t\t<dd>0.0880281690140845</dd>\n",
       "\t<dt>cad.2</dt>\n",
       "\t\t<dd>0.911971830985915</dd>\n",
       "\t<dt>appet.1</dt>\n",
       "\t\t<dd>0.77112676056338</dd>\n",
       "\t<dt>appet.2</dt>\n",
       "\t\t<dd>0.22887323943662</dd>\n",
       "\t<dt>pe.1</dt>\n",
       "\t\t<dd>0.204225352112676</dd>\n",
       "\t<dt>pe.2</dt>\n",
       "\t\t<dd>0.795774647887324</dd>\n",
       "\t<dt>ane.1</dt>\n",
       "\t\t<dd>0.158450704225352</dd>\n",
       "\t<dt>ane.2</dt>\n",
       "\t\t<dd>0.841549295774648</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[age] 52.2887323943662\n",
       "\\item[bp] 75.7957746478873\n",
       "\\item[pcc.1] 0.112676056338028\n",
       "\\item[pcc.2] 0.887323943661972\n",
       "\\item[ba.1] 0.0633802816901408\n",
       "\\item[ba.2] 0.936619718309859\n",
       "\\item[bu] 58.2753521126761\n",
       "\\item[sc] 2.08890845070423\n",
       "\\item[htn.1] 0.411971830985915\n",
       "\\item[htn.2] 0.588028169014085\n",
       "\\item[dm.1] 0.380281690140845\n",
       "\\item[dm.2] 0.619718309859155\n",
       "\\item[cad.1] 0.0880281690140845\n",
       "\\item[cad.2] 0.911971830985915\n",
       "\\item[appet.1] 0.77112676056338\n",
       "\\item[appet.2] 0.22887323943662\n",
       "\\item[pe.1] 0.204225352112676\n",
       "\\item[pe.2] 0.795774647887324\n",
       "\\item[ane.1] 0.158450704225352\n",
       "\\item[ane.2] 0.841549295774648\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "age\n",
       ":   52.2887323943662bp\n",
       ":   75.7957746478873pcc.1\n",
       ":   0.112676056338028pcc.2\n",
       ":   0.887323943661972ba.1\n",
       ":   0.0633802816901408ba.2\n",
       ":   0.936619718309859bu\n",
       ":   58.2753521126761sc\n",
       ":   2.08890845070423htn.1\n",
       ":   0.411971830985915htn.2\n",
       ":   0.588028169014085dm.1\n",
       ":   0.380281690140845dm.2\n",
       ":   0.619718309859155cad.1\n",
       ":   0.0880281690140845cad.2\n",
       ":   0.911971830985915appet.1\n",
       ":   0.77112676056338appet.2\n",
       ":   0.22887323943662pe.1\n",
       ":   0.204225352112676pe.2\n",
       ":   0.795774647887324ane.1\n",
       ":   0.158450704225352ane.2\n",
       ":   0.841549295774648\n",
       "\n"
      ],
      "text/plain": [
       "        age          bp       pcc.1       pcc.2        ba.1        ba.2 \n",
       "52.28873239 75.79577465  0.11267606  0.88732394  0.06338028  0.93661972 \n",
       "         bu          sc       htn.1       htn.2        dm.1        dm.2 \n",
       "58.27535211  2.08890845  0.41197183  0.58802817  0.38028169  0.61971831 \n",
       "      cad.1       cad.2     appet.1     appet.2        pe.1        pe.2 \n",
       " 0.08802817  0.91197183  0.77112676  0.22887324  0.20422535  0.79577465 \n",
       "      ane.1       ane.2 \n",
       " 0.15845070  0.84154930 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>age</dt>\n",
       "\t\t<dd>15.3722162228823</dd>\n",
       "\t<dt>bp</dt>\n",
       "\t\t<dd>14.2149270731376</dd>\n",
       "\t<dt>pcc.1</dt>\n",
       "\t\t<dd>0.316754557125878</dd>\n",
       "\t<dt>pcc.2</dt>\n",
       "\t\t<dd>0.316754557125878</dd>\n",
       "\t<dt>ba.1</dt>\n",
       "\t\t<dd>0.244075778431375</dd>\n",
       "\t<dt>ba.2</dt>\n",
       "\t\t<dd>0.244075778431375</dd>\n",
       "\t<dt>bu</dt>\n",
       "\t\t<dd>49.799424105288</dd>\n",
       "\t<dt>sc</dt>\n",
       "\t\t<dd>5.99130629338292</dd>\n",
       "\t<dt>htn.1</dt>\n",
       "\t\t<dd>0.493058872962688</dd>\n",
       "\t<dt>htn.2</dt>\n",
       "\t\t<dd>0.493058872962688</dd>\n",
       "\t<dt>dm.1</dt>\n",
       "\t\t<dd>0.486312938062693</dd>\n",
       "\t<dt>dm.2</dt>\n",
       "\t\t<dd>0.486312938062693</dd>\n",
       "\t<dt>cad.1</dt>\n",
       "\t\t<dd>0.283836013564045</dd>\n",
       "\t<dt>cad.2</dt>\n",
       "\t\t<dd>0.283836013564045</dd>\n",
       "\t<dt>appet.1</dt>\n",
       "\t\t<dd>0.420849046892993</dd>\n",
       "\t<dt>appet.2</dt>\n",
       "\t\t<dd>0.420849046892993</dd>\n",
       "\t<dt>pe.1</dt>\n",
       "\t\t<dd>0.40384603990709</dd>\n",
       "\t<dt>pe.2</dt>\n",
       "\t\t<dd>0.40384603990709</dd>\n",
       "\t<dt>ane.1</dt>\n",
       "\t\t<dd>0.365807680407942</dd>\n",
       "\t<dt>ane.2</dt>\n",
       "\t\t<dd>0.365807680407942</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[age] 15.3722162228823\n",
       "\\item[bp] 14.2149270731376\n",
       "\\item[pcc.1] 0.316754557125878\n",
       "\\item[pcc.2] 0.316754557125878\n",
       "\\item[ba.1] 0.244075778431375\n",
       "\\item[ba.2] 0.244075778431375\n",
       "\\item[bu] 49.799424105288\n",
       "\\item[sc] 5.99130629338292\n",
       "\\item[htn.1] 0.493058872962688\n",
       "\\item[htn.2] 0.493058872962688\n",
       "\\item[dm.1] 0.486312938062693\n",
       "\\item[dm.2] 0.486312938062693\n",
       "\\item[cad.1] 0.283836013564045\n",
       "\\item[cad.2] 0.283836013564045\n",
       "\\item[appet.1] 0.420849046892993\n",
       "\\item[appet.2] 0.420849046892993\n",
       "\\item[pe.1] 0.40384603990709\n",
       "\\item[pe.2] 0.40384603990709\n",
       "\\item[ane.1] 0.365807680407942\n",
       "\\item[ane.2] 0.365807680407942\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "age\n",
       ":   15.3722162228823bp\n",
       ":   14.2149270731376pcc.1\n",
       ":   0.316754557125878pcc.2\n",
       ":   0.316754557125878ba.1\n",
       ":   0.244075778431375ba.2\n",
       ":   0.244075778431375bu\n",
       ":   49.799424105288sc\n",
       ":   5.99130629338292htn.1\n",
       ":   0.493058872962688htn.2\n",
       ":   0.493058872962688dm.1\n",
       ":   0.486312938062693dm.2\n",
       ":   0.486312938062693cad.1\n",
       ":   0.283836013564045cad.2\n",
       ":   0.283836013564045appet.1\n",
       ":   0.420849046892993appet.2\n",
       ":   0.420849046892993pe.1\n",
       ":   0.40384603990709pe.2\n",
       ":   0.40384603990709ane.1\n",
       ":   0.365807680407942ane.2\n",
       ":   0.365807680407942\n",
       "\n"
      ],
      "text/plain": [
       "       age         bp      pcc.1      pcc.2       ba.1       ba.2         bu \n",
       "15.3722162 14.2149271  0.3167546  0.3167546  0.2440758  0.2440758 49.7994241 \n",
       "        sc      htn.1      htn.2       dm.1       dm.2      cad.1      cad.2 \n",
       " 5.9913063  0.4930589  0.4930589  0.4863129  0.4863129  0.2838360  0.2838360 \n",
       "   appet.1    appet.2       pe.1       pe.2      ane.1      ane.2 \n",
       " 0.4208490  0.4208490  0.4038460  0.4038460  0.3658077  0.3658077 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "13"
      ],
      "text/latex": [
       "13"
      ],
      "text/markdown": [
       "13"
      ],
      "text/plain": [
       "[1] 13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "20"
      ],
      "text/latex": [
       "20"
      ],
      "text/markdown": [
       "20"
      ],
      "text/plain": [
       "[1] 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data <- scale(train_data)\n",
    "col_means_train <- attr(train_data, \"scaled:center\")\n",
    "col_stddevs_train <- attr(train_data, \"scaled:scale\")\n",
    "test_data <- scale(test_data, center = col_means_train, scale = col_stddevs_train)\n",
    "attr(test_data, \"scaled:center\")\n",
    "attr(test_data, \"scaled:scale\")\n",
    "ncol(data)\n",
    "ncol(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Set session seed to 1 (disabled GPU, CPU parallelism)\n"
     ]
    }
   ],
   "source": [
    "use_session_with_seed(1)\n",
    "\n",
    "model <- keras_model_sequential(layers=list(\n",
    "    layer_dense(units = 11, activation = \"relu\",\n",
    "    input_shape = dim(train_data)[2]),\n",
    "    layer_dense(units = 2, activation = \"softmax\")\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "compile(model,\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    optimizer = \"adam\",\n",
    "    metrics = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "................................................................................\n",
      "...................."
     ]
    }
   ],
   "source": [
    "print_dot_callback <- callback_lambda(\n",
    "    on_epoch_end = function(epoch, logs) {\n",
    "        if (epoch %% 80 == 0) cat(\"\\n\")\n",
    "        cat(\".\")\n",
    "    }\n",
    ")\n",
    "\n",
    "early_stop <- callback_early_stopping(monitor = \"val_loss\", patience = 20)\n",
    "\n",
    "history <- fit(model,\n",
    "    train_data, train_labels,\n",
    "    epochs = 100,\n",
    "    batch_size = 20,\n",
    "    validation_split = 0.2,\n",
    "    verbose = 0,\n",
    "    callbacks = list(early_stop, print_dot_callback))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trained on 227 samples (batch_size=20, epochs=100)\n",
       "Final epoch (plot to see history):\n",
       "    loss: 0.09867\n",
       "     acc: 0.9692\n",
       "val_loss: 0.2772\n",
       " val_acc: 0.8947 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CJQ7AAAAABMBIodAAAAgIlAsWPTo0eP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\nm47H41HDo6QcDnE41PBE8yMNiIjLpdrGDZQqU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+yz7p072dulFpfMP3OhWCASV1ZE+vtZSsT7klMPZmQfPHhQh9eOPK64uLhXr14ZAZ1p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\n7AAAABSCYAcAAKAQ/w+/wQ8BWzSmuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history\n",
    "plot(history, smooth=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.239046931371722</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.901408433914185</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.239046931371722\n",
       "\\item[\\$acc] 0.901408433914185\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.239046931371722\n",
       "$acc\n",
       ":   0.901408433914185\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.2390469\n",
       "\n",
       "$acc\n",
       "[1] 0.9014084\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval.results <- evaluate(model,\n",
    "    test_data,\n",
    "    test_labels,\n",
    "    verbose = 0)\n",
    "\n",
    "eval.results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}