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Heart Disease Prediction using Machine Learning Project

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Heart diseases is a term covering any disorder of the heart. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases have increased significantly over the past few decades in India, in fact it has become the leading cause of death in India.

A study shows that from 1990 to 2016 the death rate due to heart diseases have increased around 34 per cent from 155.7 to 209.1 deaths per one lakh population in India.

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

Problem Description :

A dataset is formed by taking into consideration some of the information of 920 individuals. The problem is : based on the given information about each individual we have to calculate that whether that individual will suffer from heart disease.

Dataset :

The Heart disease data set consists of patient data from Cleveland, Hungary, Long Beach and Switzerland. The combined dataset consists of 14 features and 916 samples with many missing values. The features used in here are,

  1. Age : displays the age of the individual.
  2. Sex : displays the gender of the individual using the following format : 1 = male 0 = female.
  3. Chest-pain type : displays the type of chest-pain experienced by the individual using the following format : 1 = typical angina 2 = atypical angina 3 = non - anginal pain 4 = asymptotic
  4. Resting Blood Pressure : displays the resting blood pressure value of an individual in mmHg (unit)
  5. Serum Cholestrol : displays the serum cholestrol in mg/dl (unit)
  6. Fasting Blood Sugar : compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false)
  7. Resting ECG : 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy
  8. Max heart rate achieved : displays the max heart rate achieved by an individual.
  9. Exercise induced angina : 1 = yes 0 = no
  10. ST depression induced by exercise relative to rest : displays the value which is integer or float.
  11. Peak exercise ST segment : 1 = upsloping 2 = flat 3 = downsloping
  12. Number of major vessels (0-3) colored by flourosopy : displays the value as integer or float.
  13. Thal : displays the thalassemia : 3 = normal 6 = fixed defect 7 = reversable defect
  14. Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not : 0 = absence 1,2,3,4 = present.

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app


  • Install requirements
    pip install -r requirements.txt
  • Run flask web app

Models used and accuracy

A Random forest classifier achieves an average multi-class classification accuracy of 56-60%(183 test samples). It gets 75-80% average binary classification accuracy(heart disease or no heart disease).

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Multiple Disease Prediction using Machine Learning

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Multiple Disease Prediction using Machine Learning . This Web App was developed using Python Flask Web Framework . The models won’t to predict the diseases were trained on large Datasets. All the links for datasets and therefore the python notebooks used for model creation are mentioned below during this readme. The WebApp can predict following Diseases:

  • Diabetes
  • Breast Cancer
  • Heart Disease
  • Kidney Disease
  • Liver Disease
  • Malaria
  • Pneumonia

Models with their Accuracy of Prediction

DiseaseType of ModelAccuracy
DiabetesMachine Learning Model98.25%
Breast CancerMachine Learning Model98.25%
Heart DiseaseMachine Learning Model85.25%
Kidney DiseaseMachine Learning Model99%
Liver DiseaseMachine Learning Model78%
MalariaDeep Learning Model(CNN)96%
PneumoniaDeep Learning Model(CNN)95%


Steps to run the WebApp in local Computer

Step-1: Download the files in the repository.
Step-2: Get into the downloaded folder, open command prompt in that directory and install all the dependencies using following command

pip install -r requirements.txt

Step-3: After successfull installation of all the dependencies, run the following command


Dataset Links

All the datasets were used from kaggle.