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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.
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.
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,
pip install -r requirements.txt
python main_file.py
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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