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<h1 class="title toc-ignore">Predicting heart disease</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#introduction" id="toc-introduction"><span
class="toc-section-number">1</span> Introduction</a>
<ul>
<li><a href="#overview" id="toc-overview"><span
class="toc-section-number">1.1</span> Overview</a></li>
<li><a href="#about-the-data" id="toc-about-the-data"><span
class="toc-section-number">1.2</span> About the Data</a></li>
</ul></li>
<li><a href="#data-exploration" id="toc-data-exploration"><span
class="toc-section-number">2</span> Data Exploration</a>
<ul>
<li><a href="#loading-relevant-packages"
id="toc-loading-relevant-packages"><span
class="toc-section-number">2.1</span> loading Relevant packages</a></li>
<li><a href="#loading-data-set" id="toc-loading-data-set"><span
class="toc-section-number">2.2</span> loading Data Set</a></li>
<li><a href="#converting-into-factors"
id="toc-converting-into-factors"><span
class="toc-section-number">2.3</span> Converting into factors</a></li>
<li><a href="#checking-missing-values"
id="toc-checking-missing-values"><span
class="toc-section-number">2.4</span> checking missing values</a></li>
</ul></li>
<li><a href="#exploratory-data-analysis"
id="toc-exploratory-data-analysis"><span
class="toc-section-number">3</span> Exploratory data analysis</a>
<ul>
<li><a href="#univariate-analysis" id="toc-univariate-analysis"><span
class="toc-section-number">3.1</span> Univariate analysis</a>
<ul>
<li><a href="#descriptives-frequency-table"
id="toc-descriptives-frequency-table"><span
class="toc-section-number">3.1.1</span> Descriptives Frequency
table</a></li>
<li><a href="#visualization" id="toc-visualization"><span
class="toc-section-number">3.1.2</span> Visualization</a></li>
</ul></li>
<li><a href="#bivariate-analysis" id="toc-bivariate-analysis"><span
class="toc-section-number">3.2</span> Bivariate analysis</a>
<ul>
<li><a href="#difference-frequency-table"
id="toc-difference-frequency-table"><span
class="toc-section-number">3.2.1</span> Difference Frequency
table</a></li>
<li><a href="#visualization-1" id="toc-visualization-1"><span
class="toc-section-number">3.2.2</span> Visualization</a></li>
<li><a href="#correlation" id="toc-correlation"><span
class="toc-section-number">3.2.3</span> Correlation</a></li>
</ul></li>
</ul></li>
<li><a href="#logistic-regression" id="toc-logistic-regression"><span
class="toc-section-number">4</span> Logistic Regression</a>
<ul>
<li><a href="#splitting-the-data-for-training-and-testing"
id="toc-splitting-the-data-for-training-and-testing"><span
class="toc-section-number">4.1</span> Splitting the data for training
and testing</a></li>
<li><a href="#model" id="toc-model"><span
class="toc-section-number">4.2</span> model</a></li>
<li><a href="#model-statistics" id="toc-model-statistics"><span
class="toc-section-number">4.3</span> model statistics</a></li>
<li><a href="#model-results" id="toc-model-results"><span
class="toc-section-number">4.4</span> model results</a></li>
</ul></li>
<li><a href="#evaluating-our-model" id="toc-evaluating-our-model"><span
class="toc-section-number">5</span> Evaluating our Model</a>
<ul>
<li><a href="#fitting-with-testing-data"
id="toc-fitting-with-testing-data"><span
class="toc-section-number">5.1</span> Fitting with testing data</a></li>
<li><a href="#confussion-matrix" id="toc-confussion-matrix"><span
class="toc-section-number">5.2</span> Confussion matrix</a></li>
<li><a href="#roc-curve" id="toc-roc-curve"><span
class="toc-section-number">5.3</span> ROC curve</a></li>
</ul></li>
</ul>
</div>
<div id="introduction" class="section level1" number="1">
<h1><span class="header-section-number">1</span> Introduction</h1>
<div id="overview" class="section level2" number="1.1">
<h2><span class="header-section-number">1.1</span> Overview</h2>
<p>Cardiovascular diseases (CVDs) are the number 1 cause of death
globally, taking an estimated 17.9 million lives each year, which
accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are
due to heart attacks and strokes, and one-third of these deaths occur
prematurely in people under 70 years of age. This makes it important for
health facilities to be able to predict occurrences of heart diseases in
order to make plans for early treatment (Assmann et al. 2005). Heart
failure is a common event caused by CVDs and this dataset contains 11
features that can be used to predict a possible heart disease.</p>
<p>People with cardiovascular disease or who are at high cardiovascular
risk (due to the presence of one or more risk factors such as
hypertension, diabetes, hyperlipidaemia or already established disease)
need early detection and management wherein a machine learning model can
be of great help.</p>
</div>
<div id="about-the-data" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> About the Data</h2>
<p>This dataset contains 918 observations with 12 features. The data
dictionary is as follows:</p>
<ul>
<li>output: target variable (1=has heart attack, 0= has no heart
attack)</li>
<li>age: Age of the patient</li>
<li>sex: Sex of the patient (1= male, 0= female).</li>
<li>cp: chest pain type (0=typical angina, 1=atypical angina,
2=non-anginal and 3=asymptomatic)</li>
<li>trtbps: resting blood pressure (in mm Hg)</li>
<li>chol: cholestoral in mg/dl fetched via BMI sensor</li>
<li>fbs: fasting blood sugar (>120mg/dl=1, <= 120mg/dl=0).</li>
<li>restecg: resting electrocardiographic results( 0=normal, 1=having
ST-T wave abnormality, 2=showing probable or definate left ventricular
hypertrophy by Este’s criteria.)</li>
<li>thalach: Maximum heart rate achieved.</li>
<li>exng: exercise induced angina; 1=yes, 0=no.</li>
<li>oldpeak: ST depression.</li>
<li>slp = slope of peak exercise; 0=upsloping, 1=flat,
2=downsloping.</li>
</ul>
</div>
</div>
<div id="data-exploration" class="section level1" number="2">
<h1><span class="header-section-number">2</span> Data Exploration</h1>
<div id="loading-relevant-packages" class="section level2" number="2.1">
<h2><span class="header-section-number">2.1</span> loading Relevant
packages</h2>
<pre class="r"><code>#Import relevant packages
library(tidyverse)
library(janitor)
#library(readr)
library(plotly)
library(knitr)</code></pre>
</div>
<div id="loading-data-set" class="section level2" number="2.2">
<h2><span class="header-section-number">2.2</span> loading Data Set</h2>
<pre class="r"><code>heart_disease <- read_csv('https://raw.githubusercontent.com/reinpmomz/Predicting-heart-disease/main/data/heart.csv')%>%
clean_names()
#View(heart_disease)
head(heart_disease)</code></pre>
<pre><code>## # A tibble: 6 x 12
## age sex cp trtbps chol fbs restecg thalach exng oldpeak slp
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 40 1 1 140 289 0 0 172 0 0 0
## 2 49 0 2 160 180 0 0 156 0 1 1
## 3 37 1 1 130 283 0 1 98 0 0 0
## 4 48 0 3 138 214 0 0 108 1 1.5 1
## 5 54 1 2 150 195 0 0 122 0 0 0
## 6 39 1 2 120 339 0 0 170 0 0 0
## # ... with 1 more variable: output <dbl></code></pre>
<pre class="r"><code>##checking the data structure
str(heart_disease)</code></pre>
<pre><code>## spc_tbl_ [918 x 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:918] 40 49 37 48 54 39 45 54 37 48 ...
## $ sex : num [1:918] 1 0 1 0 1 1 0 1 1 0 ...
## $ cp : num [1:918] 1 2 1 3 2 2 1 1 3 1 ...
## $ trtbps : num [1:918] 140 160 130 138 150 120 130 110 140 120 ...
## $ chol : num [1:918] 289 180 283 214 195 339 237 208 207 284 ...
## $ fbs : num [1:918] 0 0 0 0 0 0 0 0 0 0 ...
## $ restecg: num [1:918] 0 0 1 0 0 0 0 0 0 0 ...
## $ thalach: num [1:918] 172 156 98 108 122 170 170 142 130 120 ...
## $ exng : num [1:918] 0 0 0 1 0 0 0 0 1 0 ...
## $ oldpeak: num [1:918] 0 1 0 1.5 0 0 0 0 1.5 0 ...
## $ slp : num [1:918] 0 1 0 1 0 0 0 0 1 0 ...
## $ output : num [1:918] 0 1 0 1 0 0 0 0 1 0 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. sex = col_double(),
## .. cp = col_double(),
## .. trtbps = col_double(),
## .. chol = col_double(),
## .. fbs = col_double(),
## .. restecg = col_double(),
## .. thalach = col_double(),
## .. exng = col_double(),
## .. oldpeak = col_double(),
## .. slp = col_double(),
## .. output = col_double()
## .. )
## - attr(*, "problems")=<externalptr></code></pre>
<p>From the output on the data structure, all of the data has been read
as numeric values(‘double’ value or a decimal type with at least two
decimal places) but some should be converted to factors since they are
categorical.</p>
</div>
<div id="converting-into-factors" class="section level2" number="2.3">
<h2><span class="header-section-number">2.3</span> Converting into
factors</h2>
<pre class="r"><code>heart_disease_final <- heart_disease%>%
mutate(output= factor(output, levels = c(0,1),
labels = c("No heart attack", "Has Heart attack")))%>%
mutate(sex= factor(sex, levels = c(0,1),
labels = c("Female", "Male")))%>%
mutate(cp= factor(cp, levels = c(0,1,2,3),
labels = c("typical angina", "atypical angina", "non-anginal", "asymptomatic")))%>%
mutate(fbs= factor(fbs, levels = c(0,1),
labels = c("<= 120mg/dl", ">120mg/dl")))%>%
mutate(restecg= factor(restecg, levels = c(0,1,2),
labels = c("normal", "having ST_T \n abnormality", "showing probable or \n definate left\n ventricular hypertrophy")))%>%
mutate(exng= factor(exng, levels = c(0,1),
labels = c("No", "Yes")))%>%
mutate(slp= factor(slp, levels = c(0,1,2),
labels = c("upsloping", "flat", "downsloping")))%>%
filter(trtbps>0)%>%
filter(chol>0)</code></pre>
<pre class="r"><code>var.labels = c(
age = "Age of the patient",
sex = "Sex of the patient",
cp = "Chest pain type",
trtbps = "Resting blood pressure (in mm Hg)",
chol = "Cholestoral in mg/dl",
fbs = "Fasting blood sugar",
restecg = "Resting electrocardiographic results",
thalach = "Maximum heart rate achieved",
exng = "Exercise induced angina",
oldpeak = "ST depression",
slp = "Slope of peak exercise",
output = "Heart disease")
heart_disease_final <- labelled::set_variable_labels(heart_disease_final, .labels = var.labels)
str(heart_disease_final)</code></pre>
<pre><code>## tibble [746 x 12] (S3: tbl_df/tbl/data.frame)
## $ age : num [1:746] 40 49 37 48 54 39 45 54 37 48 ...
## ..- attr(*, "label")= chr "Age of the patient"
## $ sex : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 2 1 2 2 1 ...
## ..- attr(*, "label")= chr "Sex of the patient"
## $ cp : Factor w/ 4 levels "typical angina",..: 2 3 2 4 3 3 2 2 4 2 ...
## ..- attr(*, "label")= chr "Chest pain type"
## $ trtbps : num [1:746] 140 160 130 138 150 120 130 110 140 120 ...
## ..- attr(*, "label")= chr "Resting blood pressure (in mm Hg)"
## $ chol : num [1:746] 289 180 283 214 195 339 237 208 207 284 ...
## ..- attr(*, "label")= chr "Cholestoral in mg/dl"
## $ fbs : Factor w/ 2 levels "<= 120mg/dl",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "label")= chr "Fasting blood sugar"
## $ restecg: Factor w/ 3 levels "normal","having ST_T \n abnormality",..: 1 1 2 1 1 1 1 1 1 1 ...
## ..- attr(*, "label")= chr "Resting electrocardiographic results"
## $ thalach: num [1:746] 172 156 98 108 122 170 170 142 130 120 ...
## ..- attr(*, "label")= chr "Maximum heart rate achieved"
## $ exng : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 1 2 1 ...
## ..- attr(*, "label")= chr "Exercise induced angina"
## $ oldpeak: num [1:746] 0 1 0 1.5 0 0 0 0 1.5 0 ...
## ..- attr(*, "label")= chr "ST depression"
## $ slp : Factor w/ 3 levels "upsloping","flat",..: 1 2 1 2 1 1 1 1 2 1 ...
## ..- attr(*, "label")= chr "Slope of peak exercise"
## $ output : Factor w/ 2 levels "No heart attack",..: 1 2 1 2 1 1 1 1 2 1 ...
## ..- attr(*, "label")= chr "Heart disease"</code></pre>
<p>The categorial columns are sex, cp, fbs, restecg, exng, slp while
continuous columns are age, trtbps, chol, thalach, oldpeak and target
variable is output</p>
</div>
<div id="checking-missing-values" class="section level2" number="2.4">
<h2><span class="header-section-number">2.4</span> checking missing
values</h2>
<pre class="r"><code>mean(is.na(heart_disease_final))</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>#which(is.na(heart_disease_final))
#which(!complete.cases(heart_disease_final))</code></pre>
<p>There were no missing values in our data set.</p>
</div>
</div>
<div id="exploratory-data-analysis" class="section level1" number="3">
<h1><span class="header-section-number">3</span> Exploratory data
analysis</h1>
<p>Exploring data is vital in giving a clue of the expected relationship
between the dependent variable and the exploratory variables (Zuur et
al. 2010).</p>
<div id="univariate-analysis" class="section level2" number="3.1">
<h2><span class="header-section-number">3.1</span> Univariate
analysis</h2>
<p>This is analysis of one variable to enable us understand the
distribution of values for a single variable.</p>
<div id="descriptives-frequency-table" class="section level3"
number="3.1.1">
<h3><span class="header-section-number">3.1.1</span> Descriptives
Frequency table</h3>
<pre class="r"><code>library(gtsummary)
library(flextable)
set_gtsummary_theme(list(
"tbl_summary-fn:percent_fun" = function(x) style_percent(x, digits = 1),
"tbl_summary-str:categorical_stat" = "{n} ({p}%)"
))
# Setting `Compact` theme
theme_gtsummary_compact()</code></pre>
<pre class="r"><code># make dataset with variables to summarize
tbl_summary(heart_disease_final,
type = list(
all_dichotomous() ~ "categorical",
all_continuous() ~ "continuous2")
, statistic = all_continuous() ~ c(
"{mean} ({sd})",
"{median} ({p25}, {p75})",
"{min}, {max}")
, digits = all_continuous() ~ 2
, missing = "always" # don't list missing data separately
,missing_text = "Missing"
) %>%
modify_header(label = "**Descriptives**") %>% # update the column header
bold_labels() %>%
italicize_levels()%>%
add_n() # add column with total number of non-missing observations</code></pre>
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