Heart Disease Prediction using Machine Learning

In this article, I will take you through how to train a model for the task of heart disease prediction using Machine Learning. I will use the Logistic Regression algorithm in machine learning to train a model to predict heart disease.

Introduction to Heart Disease Prediction

Predicting and diagnosing heart disease is the biggest challenge in the medical industry and relies on factors such as the physical examination, symptoms and signs of the patient.

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Factors that influence heart disease are body cholesterol levels, smoking habit and obesity, family history of illnesses, blood pressure, and work environment. Machine learning algorithms play an essential and precise role in the prediction of heart disease.

Advances in technology allow machine language to combine with Big Data tools to manage unstructured and exponentially growing data. Heart disease is seen as the world’s deadliest disease of human life. In particular, in this type of disease, the heart is not able to push the required amount of blood to the remaining organs of the human body to perform regular functions.

Heart disease can be predicted based on various symptoms such as age, gender, heart rate, etc. and reduces the death rate of heart patients.

Due to the increasing use of technology and data collection, we can now predict heart disease using machine learning algorithms. Now let’s go further with the task of heart disease prediction using machine learning with Python.

Heart Disease Prediction Using Machine Learning

Now in this section, I will take you through the task of Heart Disease Prediction using machine learning by using the Logistic regression algorithm. As I am going to use the Python programming language for this task of heart disease prediction so let’s start by importing some necessary libraries: 

The dataset that I am using here can be easily downloaded from here. Now let’s import the data and move further:

Exploratory Data Analysis

Before training the logistic regression we need to observe and analyse the data to see what we are going to work with. The goal here is to learn more about the data and become a topic export on the dataset you are working with.

EDA helps us find answers to some important questions such as: What question (s) are you trying to solve? What kind of data do we have and how do we handle the different types? What is missing in the data and how do you deal with it? Where are the outliers and why should you care? How can you add, change, or remove features to get the most out of your data?

Now let’s start with exploratory data analysis:

We have 165 people with heart disease and 138 people without heart disease, so our problem is balanced.

age         0
sex         0
cp          0
trestbps    0
chol        0
fbs         0
restecg     0
thalach     0
exang       0
oldpeak     0
slope       0
ca          0
thal        0
target      0
dtype: int64

This dataset looks perfect to use as we don’t have null values.

==============================
age : [63 37 41 56 57 44 52 54 48 49 64 58 50 66 43 69 59 42 61 40 71 51 65 53
 46 45 39 47 62 34 35 29 55 60 67 68 74 76 70 38 77]
==============================
sex : [1 0]
==============================
cp : [3 2 1 0]
==============================
trestbps : [145 130 120 140 172 150 110 135 160 105 125 142 155 104 138 128 108 134
 122 115 118 100 124  94 112 102 152 101 132 148 178 129 180 136 126 106
 156 170 146 117 200 165 174 192 144 123 154 114 164]
==============================
chol : [233 250 204 236 354 192 294 263 199 168 239 275 266 211 283 219 340 226
 247 234 243 302 212 175 417 197 198 177 273 213 304 232 269 360 308 245
 208 264 321 325 235 257 216 256 231 141 252 201 222 260 182 303 265 309
 186 203 183 220 209 258 227 261 221 205 240 318 298 564 277 214 248 255
 207 223 288 160 394 315 246 244 270 195 196 254 126 313 262 215 193 271
 268 267 210 295 306 178 242 180 228 149 278 253 342 157 286 229 284 224
 206 167 230 335 276 353 225 330 290 172 305 188 282 185 326 274 164 307
 249 341 407 217 174 281 289 322 299 300 293 184 409 259 200 327 237 218
 319 166 311 169 187 176 241 131]
==============================
fbs : [1 0]
==============================
restecg : [0 1 2]
==============================
thalach : [150 187 172 178 163 148 153 173 162 174 160 139 171 144 158 114 151 161
 179 137 157 123 152 168 140 188 125 170 165 142 180 143 182 156 115 149
 146 175 186 185 159 130 190 132 147 154 202 166 164 184 122 169 138 111
 145 194 131 133 155 167 192 121  96 126 105 181 116 108 129 120 112 128
 109 113  99 177 141 136  97 127 103 124  88 195 106  95 117  71 118 134
  90]
==============================
exang : [0 1]
==============================
oldpeak : [2.3 3.5 1.4 0.8 0.6 0.4 1.3 0.  0.5 1.6 1.2 0.2 1.8 1.  2.6 1.5 3.  2.4
 0.1 1.9 4.2 1.1 2.  0.7 0.3 0.9 3.6 3.1 3.2 2.5 2.2 2.8 3.4 6.2 4.  5.6
 2.9 2.1 3.8 4.4]
==============================
slope : [0 2 1]
==============================
ca : [0 2 1 3 4]
==============================
thal : [1 2 3 0]
==============================
target : [1 0]

Observations from the above plot:

1. cp {Chest pain}: People with cp 1, 2, 3 are more likely to have heart disease than people with cp 0.
2. restecg {resting EKG results}: People with a value of 1 (reporting an abnormal heart rhythm, which can range from mild symptoms to severe problems) are more likely to have heart disease.
3. exang {exercise-induced angina}: people with a value of 0 (No ==> angina induced by exercise) have more heart disease than people with a value of 1 (Yes ==> angina induced by exercise)
4. slope {the slope of the ST segment of peak exercise}: People with a slope value of 2 (Downslopins: signs of an unhealthy heart) are more likely to have heart disease than people with a slope value of 2 slope is 0 (Upsloping: best heart rate with exercise) or 1 (Flatsloping: minimal change (typical healthy heart)).
5. ca {number of major vessels (0-3) stained by fluoroscopy}: the more blood movement the better, so people with ca equal to 0 are more likely to have heart disease.
6. thal {thalium stress result}: People with a thal value of 2 (defect corrected: once was a defect but ok now) are more likely to have heart disease.

Observations from the above plot:

1. trestbps: resting blood pressure anything above 130-140 is generally of concern
2. chol: greater than 200 is of concern.
3. thalach: People with a maximum of over 140 are more likely to have heart disease.
4. the old peak of exercise-induced ST depression vs. rest looks at heart stress during exercise an unhealthy heart will stress more.

Correlation Matrix

Observations from correlation:

1. fbs and chol are the least correlated with the target variable.
2. All other variables have a significant correlation with the target variable.

Data Processing

After exploring the dataset, we can observe that we need to convert some categorical variables to dummy variables and scale all values before training the machine learning models.

So, for this task, I’ll use the get_dummies method to create dummy columns for categorical variables:

Applying Logistic Regression

Now, I will train a machine learning model for the task of heart disease prediction. I will use the logistic regression algorithm as I mentioned at the beginning of the article. 

But before training the model I will first define a helper function for printing the classification report of the performance of the machine learning model:

Now let’s split the data into training and test sets. I will split the data into 70% training and 30% testing:

Now let’s train the machine learning model and print the classification report of our logistic regression model:

Train Result:
================================================
Accuracy Score: 86.79%
_______________________________________________
CLASSIFICATION REPORT:
              0      1  accuracy  macro avg  weighted avg
precision  0.88   0.86      0.87       0.87          0.87
recall     0.82   0.90      0.87       0.86          0.87
f1-score   0.85   0.88      0.87       0.87          0.87
support   97.00 115.00      0.87     212.00        212.00
_______________________________________________
Confusion Matrix: 
 [[ 80  17]
 [ 11 104]]

Test Result:
================================================
Accuracy Score: 86.81%
_______________________________________________
CLASSIFICATION REPORT:
              0     1  accuracy  macro avg  weighted avg
precision  0.87  0.87      0.87       0.87          0.87
recall     0.83  0.90      0.87       0.86          0.87
f1-score   0.85  0.88      0.87       0.87          0.87
support   41.00 50.00      0.87      91.00         91.00
_______________________________________________
Confusion Matrix: 
 [[34  7]
 [ 5 45]]

Model	             Training Accuracy %	    Testing Accuracy %
Logistic Regression	          86.79	                     86.81

As you can see the model performs very well of the test set as it is giving almost the same accuracy in the test set as in the training set.

So I hope you liked this article on how to train a machine learning model for the task of heart disease prediction using machine learning. Feel free to ask your valuable questions in the comments section below.