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Heart Attack Prediction With Machine learning

Pushkar
Codersarts Read
Published in
4 min readMar 10, 2023

Overview

In this article, we’ll explore a Heart Attack Risk prediction model built using Python. We’ll discuss the dataset used, the techniques employed, and the evaluation moel. Additionally, we’ll provide links to video tutorials that walk through the process of building this model.

Introduction

Heart diseases have become one of the leading causes of death in the world. To tackle this issue, we have created a heart attack prediction model using machine learning algorithms. In this project, we aim to predict whether a patient is at risk of a heart attack or not based on various factors such as age, gender, blood pressure, cholesterol levels, etc.

Dataset Overview

Dataset We have used the Heart Disease dataset, which is available on the Kaggle platform. This dataset contains information about 303 patients and their health condition. It consists of 14 columns, where each column represents a different variable, and each row represents a unique instance.

The variables used in this dataset are:

  1. Age: Age of the patient
  2. Sex: Gender of the patient
  3. Cp: Chest pain type (1, 2, 3, or 4)
  4. Trtbps: Resting blood pressure (in mm Hg)
  5. Chol: Cholesterol levels (in mg/dl)
  6. Fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
  7. Restecg: Resting electrocardiographic results (0, 1, or 2)
  8. Thalachh: Maximum heart rate achieved
  9. Exng: Exercise-induced angina (1 = yes; 0 = no)
  10. oldpeak : previous peak
  11. Slp: Slope of the peak exercise ST segment (1, 2, or 3)
  12. Caa: Number of major vessels (0–3)
  13. Thall: Thal rate (1, 2, or 3)
  14. Output: Target variable (0 = no heart disease, 1 = heart disease)

dataset link : https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset

Technique used

  1. Exploratory Data Analysis: In this step, the heart dataset was loaded and analyzed using Python libraries such as Pandas, NumPy, and Matplotlib. The dataset was checked for missing values, outliers, and data types. Data visualization techniques such as histograms, box plots, scatter plots, and correlation matrices were used to understand the distribution and relationship between the variables.
  2. Data Cleaning: In this step, the dataset was cleaned by handling missing values, outliers, and categorical variables. Missing values were imputed using the mean or median of the column, and outliers were removed using the interquartile range method. Categorical variables were encoded using the one-hot encoding technique.
  3. Feature Engineering: In this step, new features were created from the existing features to improve the model’s performance. The new features were created using domain knowledge and statistical techniques. For example, the body mass index (BMI) feature was created using the weight and height features.
  4. Model Building: In this step, four different machine learning algorithms were used to build the Heart Attack Prediction Model. The algorithms used were Random Forest Classifier, Support Vector Machine, K-Nearest Neighbour, and Logistic Regression. Each algorithm was trained using the cleaned and feature-engineered dataset, and the hyperparameters were tuned to get the best performance.
  5. Model Evaluation: In this step, the performance of each algorithm was evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The performance of each algorithm was compared, and the best algorithm was selected based on the evaluation metrics.

We have used four machine learning algorithms to build the heart attack prediction model:

  1. Random Forest Classifier
  2. Support Vector Machine
  3. K-Nearest Neighbour
  4. Logistic Regression

We have created a complete playlist of This project videos, which includes Heart Attack Prediction Project Part 1 and Part 2. In Part 1, we provide an introduction to the project and explain the procedures of the coming project. In Part 2, we create a complete project on the Kaggle Community Platform regarding the prediction of heart attack risk in patients based on their various test results.

Part 1 Video Link:

Part 2 Video Link:

Conclusion

In this article we have explained how to created a heart attack prediction model using machine learning algorithms. This model can help doctors and healthcare professionals to identify patients who are at risk of a heart attack and take necessary measures to prevent it. We hope this article and accompanying video tutorials have provided useful insights and techniques for building your own Heart Attack Risk prediction model using Python. The source code and dataset are available on the Kaggle platform.

Complete Code :

Github: https://github.com/CodersArts/Machine-Learning/tree/master/projects/Heart_Attack_Risk_Prediction

Thank you.

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Machine Learning
Machine Learning Ai
Data Analysis
Python

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Pushkar
Codersarts Read

Written by Pushkar

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