Heart Disease Prediction using ANN Last Updated : 04 Sep, 2024 Comments Improve Suggest changes Like Article Like Report Deep Learning is a technology of which mimics a human brain in the sense that it consists of multiple neurons with multiple layers like a human brain. The network so formed consists of an input layer, an output layer, and one or more hidden layers. The network tries to learn from the data that is fed into it and then performs predictions accordingly. The most basic type of neural network is the ANN (Artificial Neural Network). The ANN does not have any special structure, it just comprises of multiple neural layers to be used for prediction. Let's build a model that predicts whether a person has heart disease or not by using ANN. About the data: In the dataset, we have 13 columns in which we are given different attributes such as sex, age, cholesterol level, etc. and we are given a target column which tells us whether that person has heart disease or not. We will keep all the columns as independent variables other than the target column because it will be our dependent variable. We will build an ANN which will predict whether a person has heart disease or not given other attributes of the person. You can find the dataset here heart disease dataset.Code: Importing Libraries Python </p><pre><code class="language-python3">import tensorflow as tf import pandas as pd import numpy as np import matplotlib.pyplot as plt import keras from keras.models import Sequential from keras.layers import Dense from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split # This code is modified by Susobhan Akhuli</code></pre><p></p><p dir="ltr"><b><strong>Code: Importing Dataset</strong></b></p><gfg-tabs data-run-ide="true" data-mode="light"><gfg-tab slot="tab">Python</gfg-tab><gfg-panel slot="panel" data-code-lang="python3"><pre><code class="language-python3"> data = pd.read_csv('heart.csv') data.head() Data Description: Python data.describe() Code: Check for null values Python data.isnull().any() Output: 0age Falsesex Falsecp Falsetrestbps Falsechol Falsefbs Falserestecg Falsethalach Falseexang Falseoldpeak Falseslope Falseca Truethal Truetarget Falsedtype: bool Assign Dependent and Independent variable Python X = data.iloc[:,:13].values y = data["target"].values Code : Split data into Train and Test dataset Python X_train,X_test,y_train, y_test = train_test_split(X,y,test_size = 0.3 , random_state = 0 ) Code: Scale the data. Python from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) Code: Building the Model Python classifier = Sequential() classifier.add(Dense(activation = "relu", input_dim = 13, units = 8, kernel_initializer = "uniform")) classifier.add(Dense(activation = "relu", units = 14, kernel_initializer = "uniform")) classifier.add(Dense(activation = "sigmoid", units = 1, kernel_initializer = "uniform")) classifier.compile(optimizer = 'adam' , loss = 'binary_crossentropy', metrics = ['accuracy'] ) Code : Fitting the Model Python classifier.fit(X_train , y_train , batch_size = 8 ,epochs = 100 ) Code : Performing prediction and rescaling Python y_pred = classifier.predict(X_test) y_pred = (y_pred > 0.5) Code: Confusion Matrix Python cm = confusion_matrix(y_test,y_pred) cm Code: Accuracy Python accuracy = (cm[0][0]+cm[1][1])/(cm[0][1] + cm[1][0] +cm[0][0] +cm[1][1]) print(accuracy*100) We will get accuracy approximately around 85%.Get complete notebook link :Notebook: click here.Dataset: click here. Comment More infoAdvertise with us Next Article IPL Score Prediction using Deep Learning K KaranGupta5 Follow Improve Article Tags : Deep Learning AI-ML-DS Neural Network AI-ML-DS With Python Similar Reads 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read Classification ProjectsWine Quality Prediction - Machine LearningHere we will predict the quality of wine on the basis of given features. We use the wine quality dataset available on Internet for free. This dataset has the fundamental features which are responsible for affecting the quality of the wine. 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