Early Disease Detection Model

A model that analyzes specific health data obtained from a downloaded dataset that predicts if a patient has or doesn't have a particular disease (in this model, a cardiovascular disease).

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Inspiration

This model was inspired by all the people suffering from heart diseases, lung diseases, and many more. While this model cannot cure them, it can help people with early disease detection so that they can get medical treatment in time.

What it does

One of the most promising areas where AI promotes social good is in healthcare. AI-powered diagnostic tools can analyze complex medical data, enabling more and more accurate disease detection by the day. This is what my early disease detection model does - It analyzes the patients' data and determines whether or not a cardiovascular disease is present. In a community where diseases have become common due to a polluted environment, lifestyle factors, and mental health, this model can be very useful

How we built it

This model was built in a jupyter notebook using Python, machine learning, and decision trees along with libraries like pandas, numpy, matplotlib, etc.

Challenges we ran into

One of the main challenges of this model was accuracy. A good accuracy rate is extremely important, especially in a model like this. Another challenge was fitting all the complex data in the model to provide good results.

Accomplishments that we're proud of

One thing I'm proud of in this model is how it has been able to classify whether a person has a heart disease with a surprising accuracy of 94%. Of course, accuracy can be improved over time with more training and testing data, but for a new model, this was an excellent accuracy rate.

What we learned

There was a lot to learn in the making of this model, and from the few mistakes made I learned more about building machine-learning models and using decision trees to do so.

What's next for Early Disease Detection Model

The Early Disease Detection Model, with more training to improve accuracy and the amount of data it can analyze, can be put to good use in taking online tests, not only with the heart disease used but many more diseases that are predictable just by supplying data obtained from medical tests.

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