Heart disease prediction using Keras Deep Learning

Heart disease could mean range of different conditions that could affect your heart. It is one of the most complex disease to predict given number of factors in your body that can potentially lead to it. Identifying and predicting it poses a great deal of challenge for doctors and researchers alike. I will attempt to take a stab at this problem using machine learning with the public dataset thats made available here at UCI Machine Learning Repository. Let’s get started.

There are 303 records in the dataset and contains 14 continuous attributes. The goal is to predict the presence of heart disease in the patient.

Here are the 14 attributes from the dataset along with their descriptions. These attributes have been narrowed down to total of 14 in the dataset from the original set of 76 .

age: The person’s age in years
sex: The person’s sex (1 = male, 0 = female)
cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)
trestbps: The person’s resting blood pressure
chol: The person’s cholesterol measurement in mg/dl
fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)
restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes’ criteria)
thalach: The person’s maximum heart rate achieved
exang: Exercise induced angina (1 = yes; 0 = no)
oldpeak: ST depression induced by exercise relative to rest (‘ST’ relates to positions on the ECG plot)
slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)
ca: The number of major vessels (0–3)
thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)
target: Heart disease (0 = no, 1 = yes)

Exploratory Analysis

Before we start the detailed data analysis, lets begin with the exploratory analysis to understand how data is distributed and extract the preliminary knowledge.

First things first, download the data from the link provided above and import the dataset to Pandas dataframe.

Download the csv file from the link provided above and upload the csv dataset file.

from google.colab import files
uploaded = files.upload()

Import the dataset to a Panda dataframe and print first 20 records.

import io
Data = pd.read_csv(io.BytesIO(uploaded['heart.csv']))
print(Data.head(20))

With data imported successfully , let’s plot the distribution between the heart disease and absence of it; indicated by the target attribute.

f = sns.countplot(x='target', data=Data)
f.set_title("Heart disease distribution")
f.set_xticklabels(['No Heart disease', 'Heart Disease'])
plt.xlabel("");

We can see from the above plot that distribution between positive and negative heart disease is almost the same. A good setup for binary classification ML problem

Next let’s extend above distribution for male and female gender.

f = sns.countplot(x='target', data=Data, hue='sex')
plt.legend(['Female', 'Male'])
f.set_title("Heart disease by gender")
f.set_xticklabels(['No Heart disease', 'Heart Disease'])
plt.xlabel("");

From above plot we can see that no heart disease distribution between male and female is skewed. We are not sure at this point what impact this will have in the final model if any or the relationship between the two at this stage. Understanding the relations between various factors that has impact on the final outcome of the heart disease is a key to this analysis. By this point we tried to identify the pattern by plotting the individual plots between different factors. There is an alternative way of doing this; correlation matrix or heat map.It is very useful to highlight the most correlated variables in a data table. In this plot, correlation coefficients are coloured according to the value:

Let’s plot a heatmap of correlation matrix as below.

heat_map = sns.heatmap(Data.corr(method='pearson'), annot=True, fmt='.2f', linewidths=2)
heat_map.set_xticklabels(heat_map.get_xticklabels(), rotation=45);
plt.rcParams["figure.figsize"] = (50,50)

Heatmap between all 14 attributes

As you can observe there is no strong correlation between any of the 14 attributes.

Building Keras Binary Classifier

After data exploration , its time to build a Keras classifier to predict the heart disease. We split the dataset into two sets: training set and testing set. To split the data, the scikit-learn library has been used , more specifically, the sklearn.model_selection.train_test_split() function is leveraged.

from sklearn.model_selection import train_test_split
Input_train, Input_test, Target_train, Target_test = train_test_split(InputScaled, Target, test_size = 0.30, random_state = 5)
print(Input_train.shape)
print(Input_test.shape)
print(Target_train.shape)
print(Target_test.shape)

Here is the size of each of above set respectively.

(212, 13)
(91, 13)
(212, 1) 
(91, 1)

We will use the Keras Sequential model.

from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(30, input_dim=13, activation='tanh'))
model.add(Dense(20, activation='tanh'))
model.add(Dense(1, activation='sigmoid'))

In the first line, we se the model as Sequential. Then, we have added the 3 fully connected Dense layers, two hidden and one output. These are defined using the Dense class. The first level has dimension of 13 which corresponds to 13 columns attributes.

tanh is used to set the activation function. The second layer has 20 neurons and the tanh activation function. Output layer has a single neuron (output) and the sigmoid activation function suited for binary classification problems.

Let’s compile and fit the model

model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.fit(Input_train, Target_train, epochs=100, verbose=1)

Compile function has 3 arguments:

• The adam optimizer: An algorithm for first-order gradient-based optimization.
• The binary_crossentropy loss function: logarithmic loss, which for a binary classification problem is defined in Keras as binary_crossentropy
• The accuracy metric: to evaluate the performance of your model during training and testing

Finally, we set the epochs=100 and let the model train.

This should take no more than few seconds if you are running on the google colab setup. We can print the model summary and evaluate the model against the test data that we kept aside before.

model.summary()
score = model.evaluate(Input_test, Target_test, verbose=0)
print('Model Accuracy = ',score[1])

Here is the output that I got,

_________________________________________________________________ Layer (type)                 Output Shape              Param #    ================================================================= dense_4 (Dense)              (None, 30)                420        _________________________________________________________________ dense_5 (Dense)              (None, 20)                620        _________________________________________________________________ dense_6 (Dense)              (None, 1)                 21         ================================================================= Total params: 1,061 Trainable params: 1,061 Non-trainable params: 0 _________________________________________________________________ Model Accuracy =  0.9010988965139284

The model when evaluated on the test data, is about 90.10% accurate.

Summary

We have trained a Keras model for classifying the heart disease based on the open source dataset. Although this result was achieved on a smaller dataset, you will be now able to apply same concepts of data exploration, feature engineering and model building on the bigger datasets. I have made code available here in a github repo, please feel free to download and experiment with it.

As always, happy learning!