Heart Disease Prediction with Machine Learning

Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmia) and heart defects you’re born with (congenital heart defects), among others.

Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data science. The amount of data in the healthcare industry is huge.

In this Data Science Project I will be applying Machine Learning techniques to classify whether a person is suffering from Heart Disease or not.

You can download the data set we need for this project from here:

Importing the required modules :

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
import seaborn as sns
import warnings

Here we will be experimenting using KNeighborsClassifier :

from sklearn.neighbors import KNeighborsClassifier

Now let’s dive into the data

df = pd.read_csv('dataset.csv')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
age         303 non-null int64
sex         303 non-null int64
cp          303 non-null int64
trestbps    303 non-null int64
chol        303 non-null int64
fbs         303 non-null int64
restecg     303 non-null int64
thalach     303 non-null int64
exang       303 non-null int64
oldpeak     303 non-null float64
slope       303 non-null int64
ca          303 non-null int64
thal        303 non-null int64
target      303 non-null int64
dtypes: float64(1), int64(13)
memory usage: 33.2 KB

Feature Selection

To get correlation of each feature in the data set

import seaborn as sns
corrmat = df.corr()
top_corr_features = corrmat.index
#plot heat map

It’s always a good practice to work with a data set where the target classes are of approximately equal size. Thus, let’s check for the same :


Data Processing

After exploring the data set, I observed that I need to convert some categorical variables into dummy variables and scale all the values before training the Machine Learning models.

First, I’ll use the get_dummies method to create dummy columns for categorical variables.

dataset = pd.get_dummies(df, columns = ['sex', 'cp', 
                                        'exang', 'slope', 
                                        'ca', 'thal'])
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']
dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])
y = dataset['target']
X = dataset.drop(['target'], axis = 1)
from sklearn.model_selection import cross_val_score
knn_scores = []
for k in range(1,21):
    knn_classifier = KNeighborsClassifier(n_neighbors = k)
plt.plot([k for k in range(1, 21)], knn_scores, color = 'red')
for i in range(1,21):
    plt.text(i, knn_scores[i-1], (i, knn_scores[i-1]))
plt.xticks([i for i in range(1, 21)])
plt.xlabel('Number of Neighbors (K)')
plt.title('K Neighbors Classifier scores for different K values')
knn_classifier = KNeighborsClassifier(n_neighbors = 12)

#Output- 0.8448387096774195

Random Forest Classifier

from sklearn.ensemble import RandomForestClassifier
randomforest_classifier= RandomForestClassifier(n_estimators=10)

#Output- 0.8113978494623655

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Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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