Group18_project_proposal.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "#    **GROUP 18: PROJECT PROPOSAL**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9eadb2-e3a8-420c-bcc8-535fcfcc61ca",
   "metadata": {},
   "source": [
    "GROUP MEMEBERS: Dua Khan, Sohbat Sandhu, Advitya Mittal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b57c98-f0c1-4c17-af1e-424ec47d0d6e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## *Impact Of Tobacco Usage On Potiential Heart Failure Patients*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137869cb-eddd-4ab3-a2b2-35157dbb2345",
   "metadata": {},
   "source": [
    "## **Introduction**"
   ]
  },
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   "id": "ec98981b-f44f-475f-8a3c-ecc9a7a5e181",
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   "source": [
    "<br>For our project, we will analyze the [UCI Heart Failure Clinical Records Dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv). \n",
    "<br>We are interested in this dataset, as it may help prevent potential heart failure in tobacco users if they receive proper kidney evaluation in the early stages. \n",
    "<br>The dataset includes information on high-risk heart failure patients. We will mainly look at patients with a history of tobacco use and analyze their serum creatinine levels. Creatinine is a waste product developed by the muscles flowing in a person's blood. The creatinine is filtered out of the blood by the kidneys. Therefore, higher serum creatinine levels in the blood can be associated with CKD (Chronic Kidney Disease). \n",
    "<br> According to [CDC](https://www.cdc.gov/kidneydisease/publications-resources/link-between-ckd-diabetes-heart-disease.html) (Centre for Disease and Prevention Control), Chronic Kidney Disease (CKD) is a considerable risk factor for heart failure. When kidneys stop filtering blood properly, more stress is put on the heart to pump the improperly filtered blood leading to heart diseases and even failure. \n",
    "<br>Thus, comparing the serum creatinine levels of high-risk heart failure tobacco users with those of non-tobacco users will help us determine whether or not tobacco use is associated with an increase in the serum creatinine levels, as stated in many studies [(Rudnick & Leonberg-Yoo, 2017)](https://pubmed.ncbi.nlm.nih.gov/28930748/). "
   ]
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## **Preliminary Results**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0242b1-da0c-44f8-aeb1-beeb0a9349a8",
   "metadata": {},
   "source": [
    "### **Data Preparation** (Import, Clean and Wrangle Data)"
   ]
  },
  {
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   "source": [
    "# Step 1: load libraries\n",
    "library(cowplot)\n",
    "library(datateachr)\n",
    "library(digest)\n",
    "library(infer)\n",
    "library(repr)\n",
    "library(taxyvr)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2348b650-2851-4651-8e6b-2f965f3f52e8",
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m299\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m13\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[32mdbl\u001b[39m (13): age, anaemia, creatinine_phosphokinase, diabetes, ejection_fractio...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age</th><th scope=col>anaemia</th><th scope=col>creatinine_phosphokinase</th><th scope=col>diabetes</th><th scope=col>ejection_fraction</th><th scope=col>high_blood_pressure</th><th scope=col>platelets</th><th scope=col>serum_creatinine</th><th scope=col>serum_sodium</th><th scope=col>sex</th><th scope=col>smoking</th><th scope=col>time</th><th scope=col>DEATH_EVENT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>75</td><td>0</td><td> 582</td><td>0</td><td>20</td><td>1</td><td>265000</td><td>1.9</td><td>130</td><td>1</td><td>0</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><td>55</td><td>0</td><td>7861</td><td>0</td><td>38</td><td>0</td><td>263358</td><td>1.1</td><td>136</td><td>1</td><td>0</td><td>6</td><td>1</td></tr>\n",
       "\t<tr><td>65</td><td>0</td><td> 146</td><td>0</td><td>20</td><td>0</td><td>162000</td><td>1.3</td><td>129</td><td>1</td><td>1</td><td>7</td><td>1</td></tr>\n",
       "\t<tr><td>50</td><td>1</td><td> 111</td><td>0</td><td>20</td><td>0</td><td>210000</td><td>1.9</td><td>137</td><td>1</td><td>0</td><td>7</td><td>1</td></tr>\n",
       "\t<tr><td>65</td><td>1</td><td> 160</td><td>1</td><td>20</td><td>0</td><td>327000</td><td>2.7</td><td>116</td><td>0</td><td>0</td><td>8</td><td>1</td></tr>\n",
       "\t<tr><td>90</td><td>1</td><td>  47</td><td>0</td><td>40</td><td>1</td><td>204000</td><td>2.1</td><td>132</td><td>1</td><td>1</td><td>8</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " age & anaemia & creatinine\\_phosphokinase & diabetes & ejection\\_fraction & high\\_blood\\_pressure & platelets & serum\\_creatinine & serum\\_sodium & sex & smoking & time & DEATH\\_EVENT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 75 & 0 &  582 & 0 & 20 & 1 & 265000 & 1.9 & 130 & 1 & 0 & 4 & 1\\\\\n",
       "\t 55 & 0 & 7861 & 0 & 38 & 0 & 263358 & 1.1 & 136 & 1 & 0 & 6 & 1\\\\\n",
       "\t 65 & 0 &  146 & 0 & 20 & 0 & 162000 & 1.3 & 129 & 1 & 1 & 7 & 1\\\\\n",
       "\t 50 & 1 &  111 & 0 & 20 & 0 & 210000 & 1.9 & 137 & 1 & 0 & 7 & 1\\\\\n",
       "\t 65 & 1 &  160 & 1 & 20 & 0 & 327000 & 2.7 & 116 & 0 & 0 & 8 & 1\\\\\n",
       "\t 90 & 1 &   47 & 0 & 40 & 1 & 204000 & 2.1 & 132 & 1 & 1 & 8 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| age &lt;dbl&gt; | anaemia &lt;dbl&gt; | creatinine_phosphokinase &lt;dbl&gt; | diabetes &lt;dbl&gt; | ejection_fraction &lt;dbl&gt; | high_blood_pressure &lt;dbl&gt; | platelets &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | serum_sodium &lt;dbl&gt; | sex &lt;dbl&gt; | smoking &lt;dbl&gt; | time &lt;dbl&gt; | DEATH_EVENT &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 75 | 0 |  582 | 0 | 20 | 1 | 265000 | 1.9 | 130 | 1 | 0 | 4 | 1 |\n",
       "| 55 | 0 | 7861 | 0 | 38 | 0 | 263358 | 1.1 | 136 | 1 | 0 | 6 | 1 |\n",
       "| 65 | 0 |  146 | 0 | 20 | 0 | 162000 | 1.3 | 129 | 1 | 1 | 7 | 1 |\n",
       "| 50 | 1 |  111 | 0 | 20 | 0 | 210000 | 1.9 | 137 | 1 | 0 | 7 | 1 |\n",
       "| 65 | 1 |  160 | 1 | 20 | 0 | 327000 | 2.7 | 116 | 0 | 0 | 8 | 1 |\n",
       "| 90 | 1 |   47 | 0 | 40 | 1 | 204000 | 2.1 | 132 | 1 | 1 | 8 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  age anaemia creatinine_phosphokinase diabetes ejection_fraction\n",
       "1 75  0        582                     0        20               \n",
       "2 55  0       7861                     0        38               \n",
       "3 65  0        146                     0        20               \n",
       "4 50  1        111                     0        20               \n",
       "5 65  1        160                     1        20               \n",
       "6 90  1         47                     0        40               \n",
       "  high_blood_pressure platelets serum_creatinine serum_sodium sex smoking time\n",
       "1 1                   265000    1.9              130          1   0       4   \n",
       "2 0                   263358    1.1              136          1   0       6   \n",
       "3 0                   162000    1.3              129          1   1       7   \n",
       "4 0                   210000    1.9              137          1   0       7   \n",
       "5 0                   327000    2.7              116          0   0       8   \n",
       "6 1                   204000    2.1              132          1   1       8   \n",
       "  DEATH_EVENT\n",
       "1 1          \n",
       "2 1          \n",
       "3 1          \n",
       "4 1          \n",
       "5 1          \n",
       "6 1          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: assign the url for heart failure clinincal records dataset\n",
    "heart_patients_url <- \"https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv\"\n",
    "\n",
    "# Step 3: read the csv file and assign the dataframe\n",
    "patients <- read_csv(heart_patients_url)\n",
    "\n",
    "# preview dataframe\n",
    "head(patients)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1724bd1-21a5-44a0-b865-1731f3de4daf",
   "metadata": {},
   "source": [
    "*Table 1. Table of clinical features of 299 admitted heart failure patients*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360e717c-79b8-48f8-ac50-fd07a605c624",
   "metadata": {},
   "source": [
    "The dataset gives clinical features of heart failure patients admitted to the Institute of Cardiology and Allied hospital at Faisalabad in Pakistan from April to December 2015. <br>\n",
    "The data set contains 13 variables which tell us about the 13 clinical features of heart failure patients:\n",
    "| Column Name              | Description Of Clinical Feature                                               |\n",
    "|--------------------------|-------------------------------------------------------------------------------|\n",
    "| age                      | Age (in yrs)                                                                  |\n",
    "| anaemia                  | Patient has anaemia (1 = \"True\", 0 = \"False\")                                 |\n",
    "| creatinine_phosphokinase | Level of the CPK enzyme in the blood (mcg/L)                                  |\n",
    "| diabetes                 | Patient has diabetes (1 = \"True\", 0 = \"False\")                                |\n",
    "| ejection_fraction        | Percentage of blood leaving the heart at each contraction (in %)              |\n",
    "| high_blood_pressure      | Patient has high blood pressure (1 = \"True\", 0 = \"False\")                     |\n",
    "| platelets                | Platelets in the blood (in kiloplatelets/mL)                                  |\n",
    "| serum_creatinine         | Level of serum creatinine in the blood (in mg/dL)                             |\n",
    "| serum_sodium             | Level of serum sodium in the blood (in mEq/L)                                 |\n",
    "| sex                      | Binary gender (1 = \"Male\", 0 = \"Female\")                                      |\n",
    "| smoking                  | Patient uses tobacco (1 = \"True\", 0 = \"False\")                                |\n",
    "| time                     | Follow-up period (in days)                                                    |\n",
    "| DEATH_EVENT              | If the patient deceased during the follow-up period (1 = \"True\", 0 = \"False\") |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf5be54-4c02-48ed-b758-1dadd583378b",
   "metadata": {},
   "source": [
    "As we are interested in the levels of serum creatinine in blood to analyze if tobacco usage decreases the amounts of serum sodium in blood. We will try to filter out the irrelvant variables in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e482b794-db78-4b35-9407-48bd5ca6702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>serum_creatinine</th><th scope=col>tobacco_users</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1.3</td><td>True</td></tr>\n",
       "\t<tr><td>2.1</td><td>True</td></tr>\n",
       "\t<tr><td>1.1</td><td>True</td></tr>\n",
       "\t<tr><td>9.4</td><td>True</td></tr>\n",
       "\t<tr><td>4.0</td><td>True</td></tr>\n",
       "\t<tr><td>0.9</td><td>True</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " serum\\_creatinine & tobacco\\_users\\\\\n",
       " <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1.3 & True\\\\\n",
       "\t 2.1 & True\\\\\n",
       "\t 1.1 & True\\\\\n",
       "\t 9.4 & True\\\\\n",
       "\t 4.0 & True\\\\\n",
       "\t 0.9 & True\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 2\n",
       "\n",
       "| serum_creatinine &lt;dbl&gt; | tobacco_users &lt;chr&gt; |\n",
       "|---|---|\n",
       "| 1.3 | True |\n",
       "| 2.1 | True |\n",
       "| 1.1 | True |\n",
       "| 9.4 | True |\n",
       "| 4.0 | True |\n",
       "| 0.9 | True |\n",
       "\n"
      ],
      "text/plain": [
       "  serum_creatinine tobacco_users\n",
       "1 1.3              True         \n",
       "2 2.1              True         \n",
       "3 1.1              True         \n",
       "4 9.4              True         \n",
       "5 4.0              True         \n",
       "6 0.9              True         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 4: remove N/A in dataset\n",
    "# select relevant columns (smoking, serum_creatinine) for our preliminary analysis\n",
    "patients <- patients %>%\n",
    "    filter(!is.na(serum_creatinine)) %>%\n",
    "    select(smoking, serum_creatinine)\n",
    "\n",
    "# Step 5: reassign values in 'smoking' column to boolean values\n",
    "smoking_patients <- patients %>%\n",
    "    filter(smoking == 1) %>%\n",
    "    mutate(tobacco_users = \"True\") %>%\n",
    "    select(-smoking)\n",
    "\n",
    "non_smoking_patients <- patients %>%\n",
    "    filter(smoking == 0) %>%\n",
    "    mutate(tobacco_users = \"False\") %>%\n",
    "    select(-smoking)\n",
    "\n",
    "# Step 6: combine resulting dataframes into one\n",
    "clean_patients <- rbind(smoking_patients, non_smoking_patients)\n",
    "head(clean_patients)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0addb7-adb1-4f07-83a8-a0126430f90c",
   "metadata": {},
   "source": [
    "*Table 2. Table of final dataframe for preliminary analysis with relevant variables.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76553029-3161-4781-9d53-7504b6798fad",
   "metadata": {},
   "source": [
    "### **Preliminary Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc4466e4-33d4-461d-8076-930e8000de4a",
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KcT/3E1nL9pjRrOP7uqDeIhic5qWx7j9uSJ8OgVS3ag0i3GZBaWpMCAGs/3PnHH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\n",
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CMwQaeHuQYDIZQ4YskxwIyPnHzcnAB7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\n+KHiS3Sxk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G3a1LAkIiJC5Sp7KlW46LU8PlVe07rC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yAJBD+nWodIYFkI8Voag6uUs48vLyWrdu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\nOD8/v7x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      "text/plain": [
       "plot without title"
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   "source": [
    "options(repr.plot.width = 8, repr.plot.height = 6)\n",
    "\n",
    "# Step 7: visualise boxplot to observe difference in serum creatinine levels accoring to tobacco usage\n",
    "serum_creatinine_boxplot <- clean_patients %>%\n",
    "    ggplot(aes(x = tobacco_users, y = serum_creatinine, fill = tobacco_users)) +\n",
    "    geom_boxplot() +\n",
    "    labs(x = \"Tobacco users\", y = \"Serum creatinine levels in blood (in mg/dL)\", fill = \"Tobacco user\") +\n",
    "    ggtitle(\"Boxplots of Serum creatinine levels in blood\") +\n",
    "    theme(text = element_text(size = 15))\n",
    "serum_creatinine_boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6197d729-9d27-4d73-9d38-565e6e136290",
   "metadata": {},
   "source": [
    "*Figure 1. Side-by-side boxplot of serum creatinine levels in blood of heart failure patients differentiated by tobacco use*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "185a0829-8466-4746-8ebf-7865b6f8b615",
   "metadata": {},
   "outputs": [
    {
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9FYrKHtSzZWfICkZObkupJYMFHLaXG8Tq/Te1lMYeFuDHUdM1t2POWlHMfBS2y/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\nRU9GCg==",
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GSP8AeokcLgghS4OSPqmgAooooAK8E/aTs5ZPGfw0n8NeKrrwv8SZLm7sdGaHRzqk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\nf5r/AN9vzqW7kcXMvzt9496r1Nd/8fUv+8aAGea/99vzo81/77fnTKKAH+a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bGzthwgQv1gQAAABAnaZM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\nat269bZt2955551p06Y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      ],
      "text/plain": [
       "plot without title"
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     },
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   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "\n",
    "# Step 9: visualise sample distibutions to observe the difference frequency in serum creatinine levels \n",
    "# accoring to tobacco usage\n",
    "sample_distribution <- clean_patients %>%\n",
    "    ggplot(aes(x = serum_creatinine, fill = tobacco_users)) +\n",
    "    geom_histogram(binwidth = 0.4, color = \"white\") +\n",
    "    facet_grid(. ~ tobacco_users) +\n",
    "    labs(x = \"Serum creatinine levels in blood (in mg/dL)\", y = \"Frequencey\", fill = \"Tobacco Users\") +\n",
    "    ggtitle(\"Sample distribution of Serum creatinine levels in blood of heart failure patients\") +\n",
    "    theme(text = element_text(size = 10))\n",
    "sample_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8367cddc-f0f3-4a28-9ac2-087fd54ba9bd",
   "metadata": {},
   "source": [
    "*Figure 2. Side-by-side sample distribution of serum creatinine levels in blood of heart failure patients differentiated by tobacco use*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68f6d05e-a58c-48e6-abd4-6eb9ae56310d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Summary Statistics for Serum Creatinine Levels of 96 Tobacco users:\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.600   0.900   1.100   1.353   1.300   9.400 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Summary Statistics for Serum Creatinine Levels for 203 Non-tobacco users:\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.500   0.900   1.100   1.413   1.500   9.000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 8: get summary statistics for above boxplot\n",
    "print(\"Summary Statistics for Serum Creatinine Levels of 96 Tobacco users:\")\n",
    "summary(smoking_patients$serum_creatinine)\n",
    "\n",
    "print(\"Summary Statistics for Serum Creatinine Levels for 203 Non-tobacco users:\")\n",
    "summary(non_smoking_patients$serum_creatinine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7981235-6a31-42d4-8b54-8ad9a02a77b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"The observed test statistics for hypothesis testing is:\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "-0.060641420361248"
      ],
      "text/latex": [
       "-0.060641420361248"
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      "text/markdown": [
       "-0.060641420361248"
      ],
      "text/plain": [
       "[1] -0.06064142"
      ]
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     "metadata": {},
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   ],
   "source": [
    "# compute the observed test statistic\n",
    "obs_diff_mean <- clean_patients %>% \n",
    "    specify(formula = serum_creatinine ~ tobacco_users) %>% \n",
    "    calculate(stat = \"diff in means\", order = c(\"True\", \"False\")) %>%\n",
    "    pull()\n",
    "print(\"The observed test statistics for hypothesis testing is:\")\n",
    "obs_diff_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75946c2-dba6-445d-b20c-04e862cb715b",
   "metadata": {},
   "source": [
    "From *Figure 1* and the corresponding summary statistics, we can observe a slight difference in the mean serum creatinine levels in the blood of tobacco users and non-users. We observe that the mean serum creatinine levels are higher in non-tobacco users.\n",
    "<br> The above boxplot and sample distribution show the presence of outliers which heavily influences the mean of the distributions, so we cannot say that the trend will remain the same for a larger population."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf619e85-e203-4f67-917f-004789e19cf1",
   "metadata": {},
   "source": [
    "## **Methods: Plan**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4392f7dd-cbc6-44ca-aa97-aabef645b6c0",
   "metadata": {},
   "source": [
    "#### Strengths & Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b904ad14-2b9e-49b4-8992-239692224c73",
   "metadata": {},
   "source": [
    "Our proposed report uses the data from 299 heart failure patients admitted to the Institute of Cardiology and Allied hospital at Faisalabad in Pakistan. Our data is from a renowned institution in Faislabad and thus increases the \"trustworthiness\" of the source. We are using scientifically proven facts regarding the association of serum creatinine levels in blood with tobacco usage. We are using the thought process in the following steps:\n",
    "1. Tobacco Usage increases the serum creatinine levels in the blood.\n",
    "2. Increased serum creatinine levels show an increased risk of being susceptible to CKD.\n",
    "3. Increasing the risk factor of CKD shows an increase in the danger of heart failure. <br>\n",
    "\n",
    "We can also observe some limitations preventing us from sharing the report with a shareholder as the results from the data is not generalizable to a population outside Pakistan as there are many factors influencing the results like\n",
    "- Worse general health due to [Poor Air Quality Index](https://www.researchgate.net/publication/271447511_A_preliminary_assessment_of_air_quality_index_AQI_along_a_busy_road_in_Faisalabad_metropolitan_Pakistan),\n",
    "- Not everyone can [access health services due to poverty](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570466/),\n",
    "- Hypertension due to a [high-paced lifestyle](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7845984/) and more. <br>\n",
    "\n",
    "Using confidence intervals to explore the uncertainty of the estimates and using more variables as explanatory variables in our hypothesis testing to observe a better association may address the gap so we can give the shareholder adequate reports for further use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edac2b94-980e-48d5-9028-94ba32020903",
   "metadata": {},
   "source": [
    "#### Statistical Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c957306-aaf1-45b8-ba58-ba3c4e017678",
   "metadata": {},
   "source": [
    "We aim to analyze a dataset of the medical records of patients having heart failure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e70884-43aa-4170-8fe2-36ba9b0131b1",
   "metadata": {},
   "source": [
    "| Variables                | Description                                                                   |\n",
    "|--------------------------|-------------------------------------------------------------------------------|\n",
    "| $H_0$                    | Null Hypothesis                                                               |\n",
    "| $H_A$                    | Alternate Hypothesis                                                           |\n",
    "| $\\mu_T$                  | Population mean of Serum Creatinine levels of Tobacco Users                   |\n",
    "| $\\mu_T$.                 | Population mean of Serum Creatinine levels of Non-tobacco Users             |\n",
    "| $\\hat{\\mu}_T$            | Sample Mean of Serum Creatinine levels of Tobacco Users                       |\n",
    "| $\\hat{\\mu}_N$            | Sample mean of Serum Creatinine levels of Non-tobacco Users                   |\n",
    "*Table 3. Variables used and their descriptions*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da95daaa-0f8d-4189-bf24-5f19410d738c",
   "metadata": {},
   "source": [
    "Our null hypothesis is that serum creatinine levels in the blood of heart failure patients are the same for those who use tobacco and those who do not :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae717a2-6b56-4c85-8eed-f0c29b70ff5a",
   "metadata": {},
   "source": [
    "<center>$H_0 : \\mu_T - \\mu_N = 0$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7189a8ae-dbd7-416a-8dcd-1ba3b5e435ab",
   "metadata": {},
   "source": [
    "Our alternative hypothesis is that serum creatinine levels in the blood of heart failure patients are higher for those who use tobacco compared to those who do not:  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302dca4b-5318-4148-a6e8-3a86355cef00",
   "metadata": {},
   "source": [
    "<center>$H_A : \\mu_T - \\mu_N > 0$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b613c95f-db61-416e-b4a4-13ef00924251",
   "metadata": {},
   "source": [
    "Our test statistic is the difference in mean serum creatine level of the patients controlled by their preference for tobacco use. Therefore, the point estimate of interest is $\\hat{\\mu}_T - \\hat{\\mu}_N$.<br>\n",
    "We will further calculate that our observed test statistic from the observed dataset is $\\hat{\\mu}_T - \\hat{\\mu}_N = -0.060641420361248$ in favour of Non-Tobacco users. <br>\n",
    "Assuming our null hypothesis is true, we will use infer workflow to create a null model for our hypothesis tests by generating permutations. Our test statistic with the null model provides us with a p-value for the t-test.<br>\n",
    "We aim to do a one-tail t-test with a significance level ($\\alpha = 0.05$) using the p-value and see if our difference in means is statistically significant. According to the p-value and the $\\alpha$, we will conclude if we are able to \"Reject $H_0$\" or \"Fail to Reject $H_0$\"\n",
    "<br> In addition, to gain further confidence in our result, we will create a corresponding bootstrap distribution for constructing a 95% Confidence Interval for the difference in means and interpret the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c06274-1f25-454f-afad-5e2c6f23b49b",
   "metadata": {},
   "source": [
    "#### Expectations and Significance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241a2fab-a9a6-474a-851f-f4d5b31735c6",
   "metadata": {},
   "source": [
    "According to the observed test statistic, we expect our tests to fail to reject the null hypothesis, inferencing that there is no difference in the means serum creatinine levels in the blood of heart failure patients according to their history of tobacco use. <br>\n",
    "The proposed report may impact potential heart failure patients' detection and prevention methods. It may help develop convenient technology for high-risk heart failure patients to keep track of their serum creatinine levels so they can take appropriate actions without depending on a third party for their welfare. <br>\n",
    "The final report may lead to further questions about whether or not does tobacco usage result in the imbalance of other serum levels like serum sodium? Excessive serum sodium levels may increase the risk of heart failure through high blood pressure or hypertension. Furthermore, low serum levels may result in a biochemical disorder that is 25% prevalent in heart failure patients called [*Hyponatremia*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6224129/#:~:text=Hyponatremia%20or%20low%20serum%20sodium,or%20reducing%20the%20stroke%20volume.)."
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    "## **References**"
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    "- Chicco, D., Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20, 16 (2020). https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5\n",
    "- Centers for Disease Control and Prevention. (2022, July 12). The surprising link between chronic kidney disease, diabetes, and heart disease. Centers for Disease Control and Prevention. Retrieved November 6, 2022, from https://www.cdc.gov/kidneydisease/publications-resources/link-between-ckd-diabetes-heart-disease.html\n",
    "- Abebe TB, Gebreyohannes EA, Tefera YG, Bhagavathula AS, Erku DA, Belachew SA, Gebresillassie BM, Abegaz TM. The prognosis of heart failure patients: Does sodium level play a significant role? PLoS One. 2018 Nov 8;13(11):e0207242. doi: 10.1371/journal.pone.0207242. Erratum in: PLoS One. 2019 Sep 19;14(9):e0223007. PMID: 30408132; PMCID: PMC6224129. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6224129/#:~:text=Hyponatremia%20or%20low%20serum%20sodium,or%20reducing%20the%20stroke%20volume\n",
    "- Javed, Wasim & Murtaza, Dr. Ghulam & Ahmad, Hamaad & Iqbal, Muhammad. (2014). A preliminary assessment of air quality index (AQI) along a busy road in Faisalabad metropolitan, Pakistan. International journal of Environmental Sciences. 5. 623-633. https://www.researchgate.net/publication/271447511_A_preliminary_assessment_of_air_quality_index_AQI_along_a_busy_road_in_Faisalabad_metropolitan_Pakistan\n",
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   ]
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