Heart Disease Prediction in Machine Learning

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Machine Learning Introduction

Supervised Machine Learning

Unsupervised Machine Learning

Miscellaneous Topics in ML

Data Pre-Processing

Heart Disease Prediction using Logistic Regression

Machine Learning has become a cornerstone of modern technology applications worldwide.
In healthcare, it can play a pivotal role in aiding physicians with quicker and more accurate diagnoses.
In this article, we focus on the Heart Disease dataset to predict patient outcomes—whether they have heart disease or are normal.
This approach promises to make healthcare more efficient by reducing the time required for diagnoses.

#Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Dataset Link: https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

#Load the dataset
df=pd.read_csv("heart.csv")
df.head()

Output:

#To get no. of rows and columns
df.shape

Output:
(1025, 14)

#To get no. of rows and columns
df.info()

< class 'pandas.core.frame.DataFrame'>
RangeIndex: 1025 entries, 0 to 1024
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0   age       1025 non-null   int64  
1   sex       1025 non-null   int64  
2   cp        1025 non-null   int64  
3   trestbps  1025 non-null   int64  
4   chol      1025 non-null   int64  
5   fbs       1025 non-null   int64  
6   restecg   1025 non-null   int64  
7   thalach   1025 non-null   int64  
8   exang     1025 non-null   int64  
9   oldpeak   1025 non-null   float64
10  slope     1025 non-null   int64  
11  ca        1025 non-null   int64  
12  thal      1025 non-null   int64  
13  target    1025 non-null   int64  
dtypes: float64(1), int64(13)
memory usage: 112.2 KB

#To get the detail statistical information about dataframe
df.describe().T

#checking missing values
df.isnull().sum()

Output:

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

#create a heatmap taking co-relation matrix
plt.figure(figsize=(15,10))
sns.heatmap(df.corr(),linewidth=.01, annot=True, cmap="winter")
plt.show()

From the above heatmap, we are observing that CP and target have a positive correlation. That means who has a large risk of chest pain results in a greater chance to have heart disease.

df.hist(figsize=(12,12)) plt.show()

#Choose X and y [X: input variables, y: output variable] X=df.iloc[:,:-1] y = df.iloc[:, -1]

#Split dataset into training and testing data from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X, y, test_size=0.25, random_state=42)

X_test

y_test

#Create a Logistic Regression Model from sklearn.linear_model import LogisticRegression model=LogisticRegression()

#trained the model model.fit(X_train,y_train)

#Model prediction over X_test data y_prediction=model.predict(X_test) print(y_prediction)

#To get the accuracy from sklearn.metrics import accuracy_score print(accuracy_score(y_test,y_prediction))

Output:
0.8054474708171206

#Model Implementation input=(62,0,0,124,209,0,1,163,0,0.0,2,0,2) arr=np.asarray(input) #print(arr) arr_reshaped=arr.reshape(1,-1) #print(arr_reshaped) pred1=model.predict(arr_reshaped) if(pred1==1): print("patient having heart disease") else: print("patient having no heart disease")

Output:
patient having heart disease

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