{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "m9mGwEBvFS3t" }, "source": [ "### Identification of all the members of the group.\n", "Name: Renan Tiago dos Santos Silva\n", "\n", "Name: Gabriel Sarti Massukado\n", "\n", "Name: Paulo Henrique Silva Araujo\n", "\n", "Name: Rafael Sander Nogueira\n", "\n", "Name: João Gabriel Lembo" ] }, { "cell_type": "markdown", "metadata": { "id": "PXJXnFK0FS3u" }, "source": [ "## Name of the dataset\n", "Dataset: Heart Disease" ] }, { "cell_type": "markdown", "metadata": { "id": "h7Y7yExIFS3v" }, "source": [ "## Abstract\n", "This dataset contains information about several people that experienced different types of chest pain, with the data mainly relating to factors that contribute towards having heart diseases, such as cholesterol, blood sugar and eletrocardiographic results.\n", "\n", "To our analysis, only a subset of the dataset was considered, that being the Cleveland database, which was already pre-processed." ] }, { "cell_type": "markdown", "metadata": { "id": "6m3yqj56FS3v" }, "source": [ "## Questions to the dataset\n", "Is there a relation between the age of the patients and the development of heart diseases? (1)\n", "\n", "Is one of the sexes more likely to have heart diseases? (2)\n", "\n", "What is the relation between the electrocardiogram result and the occurrence of heart attacks? (3)\n", "\n", "Do physical exercises have any impact in the chance of developing heart diseases? (4)\n", "\n", "Which data is more effective in detecting the possibility of heart diseases? (5)\n", "\n", "The questions will be expanded upon during the analysis and will be referenced by their number.\n", "\n", "Note: the other questions made in the previous exercise were removed since they relied on some data that is not available in this version of the dataset.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qtfkpeYXFS3w" }, "source": [ "## EDA\n", "Present your EDA strategy:\n", "Our strategy consists in observing basic information about the dataset first, such as the mean, variance, min and max values, etc, of our data. Then, we want to check some information about the age and sex of the patients, and then search for correlation between some of the data and the 'num' value, which is the chance of developing heart diseases. All these steps are more documented together with the upcoming analysis." ] }, { "cell_type": "markdown", "metadata": { "id": "josgukkKxFfs" }, "source": [ "## Analysis/Visualizations:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 612 }, "id": "jD1HaBCMI2nN", "outputId": "c584301d-5b14-42b9-c390-0b96bb59fee0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 303 entries, 0 to 302\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 303 non-null float64\n", " 1 sex 303 non-null float64\n", " 2 cp 303 non-null float64\n", " 3 trestbps 303 non-null float64\n", " 4 chol 303 non-null float64\n", " 5 fbs 303 non-null float64\n", " 6 restecg 303 non-null float64\n", " 7 thalach 303 non-null float64\n", " 8 exang 303 non-null float64\n", " 9 oldpeak 303 non-null float64\n", " 10 slope 303 non-null float64\n", " 11 ca 303 non-null object \n", " 12 thal 303 non-null object \n", " 13 num 303 non-null int64 \n", "dtypes: float64(11), int64(1), object(2)\n", "memory usage: 33.3+ KB\n", " age sex cp trestbps chol fbs ... exang oldpeak slope ca thal num\n", "0 63.0 1.0 1.0 145.0 233.0 1.0 ... 0.0 2.3 3.0 0.0 6.0 0\n", "1 67.0 1.0 4.0 160.0 286.0 0.0 ... 1.0 1.5 2.0 3.0 3.0 2\n", "2 67.0 1.0 4.0 120.0 229.0 0.0 ... 1.0 2.6 2.0 2.0 7.0 1\n", "3 37.0 1.0 3.0 130.0 250.0 0.0 ... 0.0 3.5 3.0 0.0 3.0 0\n", "4 41.0 0.0 2.0 130.0 204.0 0.0 ... 0.0 1.4 1.0 0.0 3.0 0\n", ".. ... ... ... ... ... ... ... ... ... ... ... ... ..\n", "298 45.0 1.0 1.0 110.0 264.0 0.0 ... 0.0 1.2 2.0 0.0 7.0 1\n", "299 68.0 1.0 4.0 144.0 193.0 1.0 ... 0.0 3.4 2.0 2.0 7.0 2\n", "300 57.0 1.0 4.0 130.0 131.0 0.0 ... 1.0 1.2 2.0 1.0 7.0 3\n", "301 57.0 0.0 2.0 130.0 236.0 0.0 ... 0.0 0.0 2.0 1.0 3.0 1\n", "302 38.0 1.0 3.0 138.0 175.0 0.0 ... 0.0 0.0 1.0 ? 3.0 0\n", "\n", "[303 rows x 14 columns]\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "#imports the dataset and prints it raw\n", "hearts = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\", names = ['age','sex','cp','trestbps','chol','fbs','restecg','thalach','exang','oldpeak','slope','ca','thal','num'])\n", "hearts.info()\n", "print(hearts)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "CwijOlOQL3kO", "outputId": "083d2ba7-fbf2-418a-d4a5-496d766104dc" }, "outputs": [ { "data": { "text/html": [ "
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agetrestbpscholthalacholdpeak
count303.000000303.000000303.000000303.000000303.000000
mean54.438944131.689769246.693069149.6072611.039604
std9.03866217.59974851.77691822.8750031.161075
min29.00000094.000000126.00000071.0000000.000000
25%48.000000120.000000211.000000133.5000000.000000
50%56.000000130.000000241.000000153.0000000.800000
75%61.000000140.000000275.000000166.0000001.600000
max77.000000200.000000564.000000202.0000006.200000
\n", "
" ], "text/plain": [ " age trestbps chol thalach oldpeak\n", "count 303.000000 303.000000 303.000000 303.000000 303.000000\n", "mean 54.438944 131.689769 246.693069 149.607261 1.039604\n", "std 9.038662 17.599748 51.776918 22.875003 1.161075\n", "min 29.000000 94.000000 126.000000 71.000000 0.000000\n", "25% 48.000000 120.000000 211.000000 133.500000 0.000000\n", "50% 56.000000 130.000000 241.000000 153.000000 0.800000\n", "75% 61.000000 140.000000 275.000000 166.000000 1.600000\n", "max 77.000000 200.000000 564.000000 202.000000 6.200000" ] }, "execution_count": 2, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#relevant measurements\n", "hearts_num = hearts.drop(columns=['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'num'])\n", "hearts_num.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 350 }, "id": "MW9bKHyiMb2Z", "outputId": "50329206-0733-4ac2-c2c3-7b12c4728d82" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "sns.distplot(hearts['age'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "IkgFFk_5lT6Y", "outputId": "62ebe3a0-8bec-43c6-dbd3-3c8f820f279d" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "hearts_by_age = hearts.copy()\n", "for index, row in hearts_by_age.iterrows():\n", " hearts_by_age.at[index, 'age'] = (row['age'] - 9) - (row['age']-9)%10 + 10 #converts people from ages x9 - (x+1)8 to age (x+1)0. Ex: 29-38 becomes 30.\n", "\n", "colors = ['red', 'blue', 'green', 'purple', 'orange']\n", "for i in range(5):\n", " hearts_by_age[hearts_by_age['num'] == i].groupby('age')['age'].plot.hist(range = (25, 75), rwidth = 0.5, bins = 5, color=colors[i])" ] }, { "cell_type": "markdown", "metadata": { "id": "1CfB83cYuP_V" }, "source": [ "First, we plot the age of the patients to get a little more knowledge of the dataset and then we check if there is any age interval where the chances of developing heart diseases are higher. By looking at the histogram (where red bars represent healthy patients, and the other colors represent different probabilities of developing the diseases), we can see that people between ages 59 and 68 are more likely to have this diseases, with over 50% of the patients having possibilities of the development while the other intervals have a rate of healthy patients of more than 50%. (1)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Z5Xue-gduZ1o" }, "source": [ "Then, we want to check if the sex of the patients can tell us something about the development of heart diseases." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 386 }, "id": "v5e4_YMHrbP9", "outputId": "1723d3b6-1871-4762-d72b-618ae0726367" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "MD = hearts.query('sex == 0 & num > 0')\n", "\n", "MN = hearts.query('sex == 0 & num == 0')\n", "\n", "HD = hearts.query('sex == 1 & num > 0')\n", "\n", "HN = hearts.query('sex == 1 & num == 0')\n", "\n", "result1 = pd.concat([MD['age'], MN['age'],HD['age'], HN['age']], axis=1, sort=False)\n", "\n", "result1.columns = ['Female patients w/ chance of disease', 'Female patients w/o chance of disease', 'Male patients w/ chance of disease', 'Male patients w/o chance of disease']\n", "\n", "sns.displot(result1, kind=\"kde\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "xMmQu3PgPPk4", "outputId": "f7936074-0326-4faa-9bf4-8f1a40468bcc" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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Ljj0dmn5GPbJ5A3qmhe2Bygppe58bNdTTALyt5SGFQFsNHJ88Hbo0sjA8ZXQLMnkjPZYIZ6CyYpeMExu1SyuuW+9YECGjlGrH3Wd7drwv8+bwvzN++tFMZdkaK0LtWp+LpYWLZZ2YQ90FImQPUWrO/2PGvHO2Zc5qeDXj3mVTlXWfAka8rWGSSAu18W6/PfYcAIchoZEvJJR9H0dGbHs0dFXxGjHMlerzM0PZG6is6CfjxEYMtebP093RJqyB/0TOCjwamjnALwacJrsepkuOQGWFphvqxcKIXVq6GCWWQSHnDMtq5wzLajSJTP/r4fH7HwtfNWSPKNL9XkssZcoBLE31SY0Yat2tcNKTWoddZ1067FrLUhFErw0vhCcH/xK6bMRh2HUxa4dJcwY41DHR7bMsESgPjaNusXrx3xZv6CDy1s4PTWudH77kjEb0yJFdH0s5KfuiG/GZ+giAPNllxEMINO8SJZ/+LVyh/DM8aXQbbLreNZFpZnWgsmJcqk9qrFB77L0B7JNdRiKEQNAnBlQ9Ebqix+LIuNERKLx/tnk1AsgNVFZotkl9LIwW6gsBvC+7DK1EBNWuFUM2/yl0VcGHkfKRgA4WuGJaOz1QWbEzlSeMafAJEb0Xy9dSQLfP092hkHCMVbZOejajsnx75o01T9n+sHQU7dgmuy6mqZT3hpy0oYyIsqDuTeUgonzgi0mnuQBkdKwPkHDOlLBRuHSq5ZPSqZZP0CJsO9+OnLNnbmjmwM9FX9P+N6eJklSf8FSt37cAmAP1t806fBnqegCPJbGuE0mLLqIsah90pWXFoCstK9Agemx+NXxB7ROhK4btR0HKf0BYwvQVaiHEXABzieg2IcSfUlTTyaRFqDvLoeYRN1rfxXcs70YOI+fTheGpDU+GZpQHkW2oHoA0lvL5+jH1Uwsh/kRE5wFwdn6PEOIfSarrRHS5yXcqEEEpRMPo26yv4VbLa217Ubj66dCloYXhqaObkcnb9+qXvq7UHYjoWQCDAHwKoGO1TgEg1aFOuyt1V4iQ0Q91Y++1PYf/tT7XuFP0XfGX8GW2V8MXjA7BapNdH/uKlIc6pi4tIvIBGC5k93957DWQ0JpoFBGBI1XitE2Ph67MeTdy1hkCGu2NyhKxIlBZcX4qTxjrMNFNUPd+lj3wI21vv2OhEPJH0WcT/prxR4QF7V8ZGb710dBVRavEcN2Nl08jKf+ZjTXUDgBbiGg1gC92wxBCfCMpVXXFY88CkJWy8xmchUTv8y2be59v2Yw2Ydn1fuTMwNzQ1aU+UTZIdm1pJuXzK2I9oSeZRcSIR1t1UwaFy6Zb1pZNt6xFk8jY+mZ4/P4/ha86vVoUS5nEn2ZSHmrjDBP12G0A2mSXYRZCQNSjZ9U/w5OP/jl0+fA62B2yazKp6kBlRUoHEMXa+t0AtbUbADIA2AA0CiES2rozTqEUnsv0iEB2NJ3xfeti5Ns/WNHrzfOq9vedFjmWO3AwiLJl12cWQh2olVKx9lN/MReY1E2HrgBwbrKK6pInKOCxR2DcxRJ1KQSEfltU0Od2+5Zd4z7ZPDWsWFv29T7v0+r+U9Cc5RgNdagw6ybSa6g7i3ZrvUZEvwTg1r6kkwpBvVNgGpmbn7einWjis1OVjLHbwhFLJJRVunfZuaV7lyFkyarf02/yuj39Jma2ZeSOBpERF9WQLeW7sMZ6+z2z06cKgLMBtCSlopPjUGvoGFHDfHuOCwAO5lG/w9lYU3gM53R83xpuyXXufvt85+630WbLqd014OIt+3qPzwtZe5Rrtk2k+aV83fhYf/Ne3unvIQABqLfgqcbP1Rr6RVHhOkE0uePzFycq4oeLu57Pn9He4Bi885WJg3e+guaswppA2fTtB4rH9I1YMqVtL2MQR1N9QuO0fgOAx74XQB/ZZZjBfotl/8X9++aC6Itx40pEhBc8GD5oEbH/Gzf06rcz4JxRXVs4cqBQrGXJqdbQXp89b0pKL4CxLpJQSkSvEtHB6MfLRFSa7OK6sFfCOU3p9pKi7Z0DDQARhSxrhtDWeI6T01gzqHzz3yZfuOyOsjM/eWRL/pGtH0BE9mtbraEdSvUJY739/juAhQCuiX5+Q/Rrqd5aZC+AMSk+p+lsysjY7suwdTke+dkpypBxW8MR6kYvQ35wx/D8DY9CgCK1heWfBsqmNTTklI2EusBGuoppo3gtxRrqIiHE3zt9/gwRzUlGQafAV2oN3F7iCIKoy9AeyqO+h3OwprDhywazeBGEUlS3cXRR3UZESGk/UDJ29a7+F4eaepaMAlGv7lduSLq9UtcR0Q0AFkU/vx5AyrcTAYc6Yf/u2WP9Iav17JO95oUJivjRCRrM4qWIiK3P/pVj++xfibCS0VTT94IV1aUXWlsz80eDKB16MnR7pf4ugD8BeATqyLIVAG5KUk0nw6FOQASI/KKo8JRXymXlNOaWt7AvngazWFgibT0H7Hn/vAF73ke7tefR6tIpq2r6XpDdbssedaI7BxPQ7ZX6VwBmCSGOAAARFQB4CGrYU6kmxeczlSftuStaFOWCU70uopBl9VDaOt4vktbTYAs15Z0WeHPCaYE30ZKRd2BX2SX+/SVjHWFrjxHJOqckKd94PtZQn9ERaAAQQhwmojOTVNPJcKi7qZXQ8kS+PebdOZ+7UBlyrj8cJiDpmw1ktR0tGbr9xZKh219EY4+SXQHnpYFDjtGlEYvN6NNEQwA+i/XFRGQBsBZAjRDisu6eNNZQK0SUf9yVWsaQwW1Q/6F4uGKcflVYsDLcaaDJqRzKo751OVjtaMDYJJb1Nb2aD5SN8D1TBgD1OWXbPnfO2Hc43zVIKBYZXaiJ2jl73pR4RpTdAcAHdQnubov1OeZhAB8T0a+J6NdQn6kfTOTE3eIJtkD9j2ZxOKwoda9n9zor3ve9OFHuY25uw64ho6r+PGnystv7jdrwWJX96I4PICIpf0ZNgD/WF0bHfVQAeDLRk8Y6S+sfRLQWwJTol2YKIbYkevJu+gSSdhM0qh8XOzaBaFK871s2ksbcshh7LULuunAEUOERX3nhER8EKHyw6Kx1u8ouaT7Wq98ZIErl9N94bYzjtf8H4GcAEt4dNebb2GiIZQW5s08A3Ci7CKP4zGbdtS4r87zuvDeikGXVUNp2nl/oZrFHgrCUHFo3puTQOqjTRMevrO4/VTRnOc7U4TTRDbG8iIguA3BQCLGO4nhEOhEjPpuul12AkdxaUrQXRN0ek/3cFGXo+BQ1mMVLnSb64bmlez9EyJLZ8OU0UbteponGFGoA5wP4BhHNgLoOXy4RPSeEuKE7JzXWhA4A8Nhzoc584al/p7AiK6vqlj7FCT+qPPF4aLWjPrUNZolos+XU7up/0ZZ9fcbnhaw9ZU0TPQzAMXvelLgCFr1S35VI67fxOvw9wXrE0U2Qzu4q1mbZsRcmKob6BZrR3uAY/NmrEyd+9LMzxq/65d4++z5aqoRb45qoooGl8QZaK8YLtepj2QXo3Qs52SsbLIomDYrLRtKYMBlzNF+Plrp+rq0LJ0/+8MdDx6757WdFhz5ZSpFQIAWn7tY+6kKIpYlcpQFjPlMDwNtQZ4qxLrQD7Q8U5vfW6niCSFk5jLad79NPg1l3ZDfuPa1885OnAcAR++lbAmWXHjqSP2QISEnGyLklSThmTIz3TA0AHrsDwAEY904jqR4oyPvgOXtu3F1YJ1MYFPueeCJcrMcGs0So00RHbtxVNr2hXrtpovtnz5sibTEPY16pPcFaeOxrAeM03qRKA1H9gtyckVoft85OfWpzsbrIQA1msVCniVaNLqqrUqeJFp+zZteAi9uaevYelcBSydKu0oBRQ616Cxzqr/l5ceF6oUFfZ1demKjQrW9qMyVTjxQRsfU5sOqcPgdWRaeJnr9iT+mFlpbMgjPjnCbaredprRjz9hsAPPZxAFbKLkNP9lot+6aV9s0DUY9kHJ+EiCx8ILzPIpBW2/W0W3sEq0unbKzpO6FXuy17dAzTRMtmz5uyOyXFdcHIoVagPlfzdjFRV/ftvXxbZsYpp1Ym4vZ/hZdesEVMTuY59Kw1w35w14BL/Pt6jys8wTTRVbPnTUntRhfHMW5DkycYgdoKzgBszMzYui3D1q3hoPFYcKEyTKTxUs2ZbcHiITv+OXHS8rtGnLvq/t0l+1d/oITbdnR6yT+lFRdl5GdqQF0Mkbu2ANxeXHQsFauH1OVS70N2rC4OcntGz+aDA0b45w+Afz7qcwZs/7xsRk2do1x6qI17pVb9G+oteFpb3Kvn2jqrJWWrrL4wUTH6z43mcht2Dx61aZ4i81m6g67+5xDR09F1xTfF9AZPMAz1ap22IkDkPkeBPZXnXD6Czgor2JPKcxrEP2QXAOgs1ACeATC9G+9JW3/Jy13RqiiDU3lOQaR8PIx2nPqVaaUZOnieBnQWaiHEMqizW2LnCW5EmnZtNRM1zcuzS1nH67kLFVc6N5h14TWX35fybWu7oqtQJ+CvsguQ4X5HwZoIkZThiIdzqeSQHetknFunnpBdQAezhPoFSNhdUKY6Ran19uopY0XXLzw/STHVOPAELHf5fctlF9HBHKH2BJsAPCa7jFS6s8SxRfb6XB8Np7NC3GAGAL+TXUBn5gi16v8AHJNdRCrssNk+/ySze+uOaUkQKR+70r7B7FOX3/eW7CI601WoiWgR1AUQhhLRHiL6Xsxv9gTroKPnmmS6taTogE7W4MKCyWnfYFYpu4Dj6SrUQojrhRB9hBA2IUSpEOKpOA/xMICmZNSmFx/2yNpYY7NKHVvc2eFcKjmYvg1m26GTbqzOdBXqhHmCB2HylvCfFTt09/8sjRvMHnT5fbqbi6q7HxAN/AFAq+wikmFBbvbHxxRF8wUQEvXRcBqThg1mNdDJCLLjmS/UnuBeAH+TXYbW2oC2hwry9blGGBGtSL8Gswdcfl+b7CK6Yr5QqzwA6mQXoaWHCvM/DiWwKH+yLbhQGZ5GDWYboONGWXOGWm0Jv0d2GVqpVyj4fE62rvcPO5JDxQfzsFZ2HSkgAPzQ5feFZRdyIuYMtepJAKtlF6GFu4scnwh1+2BdWzRJscmuIQWecvl9ul533ryhVldGmQ1Ad62T8dhjtdQs75Glmy6sk1nhorNCCqpl15FEtQDull3EqZg31ADgCa6Fwbu4bi0pCuhwN8euEdFHw2mn7DKS6G6X3xffLEIJzB1q1T1Qf8MazvrMDN9OW/LXHdPSgguVEQJol11HEnwE4O+yi4iF+UPtCR4BcJvsMrpjTklRi6QdG7vtaDYVHcgz3QizENTGMUMsvWv+UAOAJ/g8DPJbtsPr2T3XHLFYpE6t7K5Fk03XYPaAy++rkl1ErNIj1KrbAKR6O9NuCQPhXxUWaLGnkxQfD6OzQgqkL8CnkaUAfim7iHikT6g9wUYA18EAQ0j/nGdf0aoop8uuo9uIaPkIMsMe4vsAXKfnPumupE+oAcAT/BQ675JoJmr6W15uShcSTIaFkw3fYBYCcK3L7zPcEtTpFWoA8ATnAnhTdhkn8ktHweoIkWZ7S8tyNJuK9ucbusHs5y6/70PZRXRH+oVadRMA3d0eHrIoh97q1fNs2XVoZdEkJZ6dIvXkVZff95DsIrorPUOtjg2/FPEuR5xkc4qLfAnsiaw7K4fRmSEFu2TXEacdAG6WXUQi0jPUAOAJbgNwBXTScLbVZvtsY2aGoQaanBIRfTiSArLLiEMTgG+6/L6g7EISkb6hBgBPcDmAWVBn3kh1W++iWr2sO6alhZOU4QZpMGsHcLXL79sgu5BEpXeoAcATfAGAW2YJH/TI2rDPajXlLpJBtcFM71MyIwBudPl9ptgamUMNAJ7ggwDmyTi1AMTdxQ6zjcD6ioWTlUzZNZzC7S6/73nZRWiFQ/2lWyFhzalnc3M+blSU4ak+byqtGqrrBrN7XH7f47KL0BKHuoO6Le5NSOFUzTag7ZGCvP6pOp80RLRMnw1m/+vy+34vuwitcag78wQFPMFbAMxNxekq1XXHzB9qAIvUEWZ6WqjvPpff99tTvYiIsohoNRFtIKLNRHR/KopLBIe6K57gHCR554Wgohx9KSd7VDLPoSfBXuTYV6CLEWYCwC9cft+vY3x9K1ehjAYAAAUhSURBVIApQohRAEYDmE5Eul6JhkN9Ip7gz5HE2Tk/LS7cIIjyknV8PVo4WZG9gksLgOtdfl/MG9oJVccebbboh/Qu0JPhUJ+MJ/grqFM2NV36drfVuufjLGOsO6al1UNodLuCgKTTHwAw2eX3vRDvG4nIQkSfAjgI4F0hxCrNq9MQh/pUPMHHAEyDhuuI31pStBtEeu/m0R4RLSsnGa3gVQDGuvy+boVRCBEWQowGUApgLBHpbpeUzjjUsfAE3wdwNtRF3BOyLjPT97nNOj7xoozp+UkpbzBbDOB8l9+X8KINQoijAJYAmJ5wVUnEoY6VJxgAcB6AuG/fOptT4mg12rpjWkpxg9lcAN9w+X0N3T0AERVRtO2DiHoAuBiAX6P6koJDHQ9PsAme4HVQh5XGvZ74a9m9Vh+1WEZrX5ixLEh+g1kjgB+4/L45Gqxa0gfAEiLaCGAN1Gdq3c7HBwASQtcNefrlsU8G8DSAgbG8PAyEx5b1D7QpNCipdRnEggdDn9vCsf3bxelDADe7/D4zrz9+Unyl7i5PcCmAM6COGT/lb8bH8u0rONBf+qCctF6YsAXAT6C2cKdtoAG+UmvDY78IwFMABnT17UaiY+eVlTZFiIpTW5h+5TaKur89Gs4mQItegNUAZrn8Pl0/66YKX6m14An+B0A51E35vubeosK1HOivqu9FhXsTbzBrg7oDy3kc6C/xlVprHvslAB4FMBQADlgsBy7q3zcbRL3kFqY/Z2+LfPqzlyPdbThcBbUxzDCL7KcKX6m15gn+G8BIAHMAHLmjxLGNA921tUOU0e0WfB7n23xQVyg5lwPdNQ51MniCoehSxKf7MjJWQm3EYV1YGnuDWTWA7wEod/l9rySxJMPj2+8UKJ9fXgrgPqirVJpuHbJE5DSJw0/ODfc6SYNZLYDfA3jc5ffpYpFIveNQp1D5/PLBUBt2rgMge8aSbvzxr6GPSutw/nFfPgbgEQAPufy+egllGRaHWoLy+eUFAL4L4H8ApH3f9ZjtkQ13vxTpmFv+GYAnADzt8vuOSCzLsDjUEpXPLyeoM8B+BKAC6dvGEXn6kdDz2S1YCOAtl98X9xBc9iUOtU6Uzy8vA3AL1MagdOnT3gngGQDzq2ZVVUuuxTQ41DpTPr88A+rVuwLADABmW8MsAOAdAAsBfFg1q4p/ADXGoda58vnlI6GGuwLq1E+jtZ43Qt24/R0A71TNqtomtxzz41AbSPn8cjuAS6CG/CKoK3Ho0QZEQwxgedWsKj2tImp6HGoDK59fng919Fr5cX+makHDvVBHePkAbIn+ualqVlVtis7PusChNqHoYJeR0Y9SAEUAHNE/CwHkAOiJrw/4iEDtH27o9Gfnv3eEeAsAX9WsKkPvDmlWHOo0Vj6/3AKgF9RwNwFo4oYr4+NQM2Yy6TrYgTHT4lAzZjIcapMiov5EtISItkQ3drtDdk0sNfiZ2qSIqA+APkKI9USUA2AdgCuFEFskl8aSjK/UJiWE2CeEWB/9ewPUrqh+cqtiqcChTgNE5ARwJtR1vZjJcahNjoiyAbwMYI4QghcbSAMcahMjIhvUQC8QQvC6XmmCG8pMitRN+OYDOCyEmCO7HpY6HGqTIqILoO4rVYUvN/O7RwixWF5VLBU41IyZDD9TM2YyHGrGTIZDzZjJcKgZMxkONWMmw6FmzGQ41IyZDIeaMZPhUDNmMhxqxkyGQ82YyXCoGTMZDjVjJsOhZsxkONSMmQyHmjGT+X/b2Gko7QwrdAAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "m = hearts[hearts['sex'] == 1].groupby('num').count()\n", "m['count'] = m['age']\n", "m.plot.pie(y = 'count', title=\"Male\")\n", "f = hearts[hearts['sex'] == 0].groupby('num').count()\n", "f['count'] = f['age']\n", "f.plot.pie(y = 'count', title=\"Female\")" ] }, { "cell_type": "markdown", "metadata": { "id": "zgyIToHwojjW" }, "source": [ "By looking at the pie charts, we can see that male patients have a higher chance of developing heart diseases (2). Now, we can look at the correlation matrix to see what other aspects influence heart diseases, so we can check if any of them are also related to the sex of the patients.\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 317 }, "id": "ULycwlPdkSAc", "outputId": "857a3a3e-a08b-495b-ff99-94f066238cfc" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light", "tags": [] }, "output_type": "display_data" } ], "source": [ "sns.heatmap(hearts.corr(), cmap = \"coolwarm\")" ] }, { "cell_type": "markdown", "metadata": { "id": "5tu2XkRPo7tY" }, "source": [ "Looking at the correlation matrix, we can see that there are 4 values (cp, exang, slope and oldpeak) that have some correlation to the 'num' value, which is the probability of developing heart diseases (5). We found it curious that cholesterol, which is tipically associated with heart problems, has no correlation with the presence of heart diseases.\n", "\n", "We can see that both the slope and oldpeak values are related to readings of the patients' electrocardiogram results. Since the oldpeak is the analysis of the depressions present in the electrocardiogram, and the slope is related to a inclination of the values read in the exam (with a downward inclination being related to the development of heart diseases), we can check that there is a relation between the results of the electrocardiogram and the development of heart diseases (3).\n", "\n", "Besides that, although we can't check (using the data available) if exercises have an influence in the development of the diseases, we can see that they are helpful in detecting this possibility, since the exang, slope and oldpeak values are all measured after exercises and have a good relation with the 'num' value. So, we don't know if they have influence but we know that we can use them to check for values that we know are correlated with the presence of heart diseases (4).\n", "\n", "Now let's compare these 4 values between male and female patients to see if there are any differences.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "dtgpuZiDn8H2", "outputId": "5cd1dd8e-e129-4a22-c885-55320f0dc40e" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "m = hearts[hearts['sex'] == 1].groupby('slope').count()\n", "m['count'] = m['age']\n", "m.plot.pie(y = 'count', title=\"Male\")\n", "\n", "\n", "f = hearts[hearts['sex'] == 0].groupby('slope').count()\n", "f['count'] = f['age']\n", "f.plot.pie(y = 'count', title=\"Feminino\")" ] }, { "cell_type": "markdown", "metadata": { "id": "opmSf1abqhO7" }, "source": [ "With the pie charts above, we can observe that there are not many differences between sexes. So, we can see that they have some influence in the development of heart diseases, but have no connection with the sex of the patients." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "CeXuPitIpN61", "outputId": "d3d9c831-f148-4945-aa71-47b6b9f2dfb5" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "m = hearts[hearts['sex'] == 1].groupby('cp').count()\n", "m['count'] = m['age']\n", "m.plot.pie(y = 'count', title=\"Male\")\n", "\n", "\n", "f = hearts[hearts['sex'] == 0].groupby('cp').count()\n", "f['count'] = f['age']\n", "f.plot.pie(y = 'count', title=\"Feminino\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "w_-OwtHdpSaE", "outputId": "766e0e8a-0a2a-4b6a-8368-689634583ae5" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "m = hearts[hearts['sex'] == 1].groupby('exang').count()\n", "m['count'] = m['age']\n", "m.plot.pie(y = 'count', title=\"Male\")\n", "\n", "\n", "f = hearts[hearts['sex'] == 0].groupby('exang').count()\n", "f['count'] = f['age']\n", "f.plot.pie(y = 'count', title=\"Female\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TFr0srFprqaE" }, "source": [ "Observing these two pie charts, we can see that both male and female patients have the same rate of angina-related chest pain, but male ones have less non-anginal pain. We believe that there is a chance that this is caused by male patients that don't inform their pain believing that it is not a problem, but we can't really assume that using the data we have available, so it stays as a supposition.\n", "However, we can see that female patients have more atypical anginal pain, which means that they only have 2 of these 3 criteria: (1) Substernal chest pain or discomfort that is (2) provoked by exertion or emotional stress and (3) relieved by rest or nitroglycerine (or both); while male have all three. But since the data are grouped, we can not know which of these 3 is not present in females so we can't analyze it further." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "DataScienceEx2.ipynb", "provenance": [] }, "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": "3.8.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "134.4px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }