notebooks/4-Random-Forest-Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest on ICAN clinical data\n",
    "\n",
    "**Objective:** implement a random forest model with the simulated ICAN clinical data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "randomForest 4.6-14\n",
      "\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "\n",
      "Attaching package: 'ggplot2'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:randomForest':\n",
      "\n",
      "    margin\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(randomForest)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2505</li>\n",
       "\t<li>28</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2505\n",
       "\\item 28\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2505\n",
       "2. 28\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2505   28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ican <- read.csv(\"simulated_ican_clinical_data.csv\", sep = \"#\", encoding = \"utf8\")\n",
    "dim(ican)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ican[\"age\"] <- as.factor(ifelse(is.na(ican$age), NA, \n",
    "                                ifelse(ican$age < 50, \"<50\", \n",
    "                                       ifelse(ican$age < 60, \"50-59\", \n",
    "                                              ifelse(ican$age < 70, \"60-69\", \n",
    "                                                     ifelse(ican$age >= 70, \">=70\", \"Other\"))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ican[\"bmi\"] <- as.factor(ifelse(is.na(ican$bmi), NA, \n",
    "                                ifelse(ican$bmi < 20, \"<20\", \n",
    "                                       ifelse(ican$bmi < 25, \"20-25\", \n",
    "                                              ifelse(ican$bmi < 30, \"25-30\", \n",
    "                                                     ifelse(ican$bmi >= 30, \">=30\", \"Other\"))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ican[\"adjusted_size_ratio\"] <- as.factor(ifelse(is.na(ican$adjusted_size_ratio), NA, \n",
    "                                                ifelse(ican$adjusted_size_ratio < 3, \"<3\", \n",
    "                                                       ifelse(ican$adjusted_size_ratio >= 3, \">=3\", \"Other\"))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ican$IA_location <- factor(as.character(ican$IA_location), levels = c(\"ICA\", \"MCA\", \"ACA\", \"PCA\"))\n",
    "ican$tobacco <- factor(as.character(ican$tobacco), levels = c(\"Non-smoker\", \"Minor_smoker\", \"Regular_smoker\"))\n",
    "ican$age <- factor(as.character(ican$age), levels = c(\"<50\", \"50-59\", \"60-69\", \">=70\"))\n",
    "ican$bmi <- factor(as.character(ican$bmi), levels = c(\"<20\", \"20-25\", \"25-30\", \">=30\"))\n",
    "ican$adjusted_size_ratio <- factor(as.character(ican$adjusted_size_ratio), levels = c(\"<3\", \">=3\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      " randomForest(formula = rupture ~ ., data = ican, importance = TRUE) \n",
      "               Type of random forest: classification\n",
      "                     Number of trees: 500\n",
      "No. of variables tried at each split: 5\n",
      "\n",
      "        OOB estimate of  error rate: 35.21%\n",
      "Confusion matrix:\n",
      "      No Yes class.error\n",
      "No  1234 312   0.2018111\n",
      "Yes  570 389   0.5943691\n"
     ]
    }
   ],
   "source": [
    "fit1 <- randomForest(rupture ~ ., data = ican, importance = TRUE)\n",
    "print(fit1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = as.data.frame(fit1$importance)\n",
    "df[\"index\"] = c(\"Sex \", \"Age \", \"Body mass index \", \"Sporadic case \", \"Multiple IA \", \"IA location \", \n",
    "                \"Adjusted size ratio \", \"Tobacco \", \"Alcohol \", \"Hypertension \", \"Headaches \", \"Diabetes \", \"Dyslipidemia \",\n",
    "                \"Ischemic stroke history \", \"Ischemic heart disease history \", \"PAD history \", \n",
    "                \"Carotid artery stenosis history \", \"Aortic aneurysm history \", \"Statin treatment \", \n",
    "                \"Platelet aggregation inhibting treatment \", \"VKA or anticoagulant treatment \", \n",
    "                \"Anti-inflammatory treatment \", \"Hormone therapy treatment \", \"Allergy \", \"Asthma \", \"Atopy \", \"Eczema \")\n",
    "df[\"importance\"] = importance(fit1, scale = TRUE, type = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+kY/ld/Umjw8PyvCWBN9XPQltrc91TlMhxNG2KmsGfsK7tEnOa3+iNgBZM/BtZi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Z2GWQDfWPwMoMpvqhILAkZWGXQTbUPwIr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\n4TG0212ugdX57XYC0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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 720,
       "width": 600
      },
      "text/plain": {
       "height": 720,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 12)\n",
    "ggplot(df, aes(x = reorder(index, importance), y = importance, fill = importance))+ \n",
    "    geom_bar(stat=\"identity\", position=\"dodge\")+ coord_flip()+\n",
    "    ylab(\"Variable Importance\\n(Mean Decrease Accuracy)\")+\n",
    "    xlab(\"\")+\n",
    "    theme(\n",
    "    axis.title.x = element_text(color=\"black\", size=16, face=\"bold\"),\n",
    "    axis.text.x = element_text(color=\"black\", size=14, face = \"bold\"),\n",
    "    axis.text.y = element_text(color=\"black\", size=14)\n",
    "    )+\n",
    "    guides(fill=F)+\n",
    "    scale_fill_gradient(low=\"lightblue\", high=\"darkblue\")"
   ]
  }
 ],
 "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
}