Machine Learning Model For Predicting Heart Disease

So as you all can see this dataset contains 14 features.

• 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 (mm Hg on admission to the hospital)
• 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. See more here)
• 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)

Steps to be followed while creating a Heart Disease Model are: —

1. Load the necessary libraries.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import rainbow
from matplotlib import rcParams
%matplotlib inline
import warnings
warnings.filterwarnings(‘ignore’)
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix#importing 3 different classifiers KNeighborsClassifier, DecisionTreeClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

2. Get the dataset description

df.describe()

df.info()

#Data Preprocessing.
dataset = pd.get_dummies(df, columns = [‘sex’, ‘cp’, ‘fbs’, ‘restecg’, ‘exang’, ‘slope’, ‘ca’, ‘thal’])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 train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

knn_classifier = KNeighborsClassifier(n_neighbors = 5, metric = ‘minkowski’, p = 2)
knn_classifier.fit(X_train, y_train)
y_pred_knn=knn_classifier.predict(X_test)
knn_classifier.score(X_test,y_test)