Predict your chance of having a heart disease because prevention is better than cure!

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Mobile Optimize

Optimized for viewing on different platforms and devices. Looks the same on every mobile, laptops, PCs and tablets. A smooth, continous flow.

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Decrease Costs

Examine your heart related reports by yourself. Predict the chance of having a heart disease free of cost.

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Smart Idea

Great accuracy. Accurate prediction rates. Analysis and prediction based on large samples of data.

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Features

Loads of features. Making it easier for anyone to predict the chance of getting heart disease. Shows analysis done on large data sets.

GitHub Page

Available on GitHub

The web page's source code is freely available on GitHub. See the code, modify and use freely under GNU GPL-3.0 licence. Just notify the changes made to author.

  • Clone at GitHub
  • Open Source
  • View, modify and use freely under GNU GPL-3.0 license

Analysis Data Set and Code Available

Data set on which the analysis is done is available. Also, the code used for analysing the data and get prediction rates is made available.

  • Clone at GitHub
  • Open Source
  • View, modify and use freely under GNU GPL-3.0 license

Analysis GitHub Page

Dataset

Vast and Reliable Dataset

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date.

  • Dataset available at UCI
  • Contains 76 attributes
  • Report on subset of 14 such attributes

Analysis Using Python and Jupyter Notebook

The code used for analysis of data and getting prediction rates is pretty simple. The code is written in Python and Jupyter Notebook.

  • Clone at GitHub
  • Easy to understand
  • Easy to modify
  • Written in Python and Jupyter Notebook

Analysis Language

Predict your Chance of Having a Heart Disease

Enter the appropriate values of symptoms you face. Get the chances of you contracting heart disease based on those values.

Heart Disease Predictor

Analysis Results Based on Dataset Available

Model's accuracy is 79.6 +- 1.4%. The following are the results of analysis done on the available heart disease dataset. Each graph shows the result based on different attributes.

Green box indicates No Disease. Red box indicates Disease.

Flow of Analysis
Age
Excercise Induced Angina
Type of Chest Pain
ST Depression Induced by Excercise
Fasting Blood Sugar
Major Vessels Colored by Fluoroscopy
Slope of Peak Excercise ST Segment
Rest ECG
Resting Blood Pressure
Serum Cholestrol
Sex
Thalium Stress Test Result
Thalium Test Max. Stress Rate

Future Scope

We can embed this application into a real time system which has sensors that measure certain attributes. This will help us get prediction and alerts in real time based on the body condition of the user.

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