Predicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm

The model's CAP curve shows we are having 100%! This means it is capable of correctly predicting 100% of patients with a heart disease after processing 50% of the data. The model's performance is "Too Good to be True"! However, with Train accuracy = 86% and Test accuracy = 82%, there is no visible sign of overfitting.

Observations

• Train accuracy: 86%

• Test accuracy: 80%

• Precision is: 86%

• The model gets confused in 11 cases

  • The model classified 3 healthy people as having a heart disease (False Positive or Type I error)
  • The model classified 8 heart disease patients as healthy people (False Negative or Type II error)

• The number of wrong predictions remains 12. However, the number of FN has dropped from 8 to 7. Which is good news for model's precision score.

• With a test accuracy of 80%, the model is not doing badly, but it's not good enough for the application of detecting heart disease

• The model was trained and tested with just 303! This is certainly not enough data to train a model for such an application. More data is needed to train and deploy the model in production.

Important Note:

With Type I error, the healthy individual would have to through treatment. If it were wrong cancer diagnose the individual would have to undergo chemotherapy with the accompanying side effects!

With Type II error, the sick individual would probably NOT get any treatment and might even die from the illness!!

The question is often asked, which of these errors is better? Well, it depends on the application. In the case of detecting illness, the goal is to make sure that no illness go undetected! The consequences of missing to detect an illness is more serious than the side effects incurred from administrying treatment on a healthy individual.

To improve your belief of having a disease or not having one, it's highly recommended to go for a second test in order to double sure about the likelihood or having the disease or free from it. There is, however, a caveat here! The second test should be from the same test provider (not the same testing clinic). This is because it's very unlikely for the same test to make an error on same patient twice! If it would. well...