Heart Disease Predictor

Heart Disease Predictor

Inspiration

I am fascinated by the intersection of healthcare and AI. This project was a great way to understand how artificial intelligence could revolutionize the healthcare industry.

What it does

With the insertion of data for 13 attributes, this model could predict whether or not a patient has heart disease.

How I built it

I built this model with a Random Forest Classifier, and found the best parameters using RandomizedSearchCV.

Challenges I ran into

Finding quality data was a bit tricky because I had to maximize the number of instances to train on while also providing enough attributes for the model to accurately predict the presence of heart disease.

Accomplishments that I'm proud of

My Random Forest Classifier was very successful in predicting heart disease and had a 90.7% accuracy score when measured on the test set.

What I learned

Understanding the problem at hand can really help in choosing a suitable model. For example, in the case of my classification problem, the Decision Tree and the Random Forest Classifier gave the highest accuracy scores, mainly because of how these models were built.