Disease Prediction by ML Model

Code and Datasets
DiseasePrediction1
DiseasePrediction2
DiseasePrediction3
Prototype

Inspiration

We were inspired by seeing that there are no chatbots that can accurately predict the disease according to the user's symptoms.

What it does

The AI health chatbot, powered by sklearn, collects user symptoms through interactive questions. Employing a decision algorithm, it generates personalized health advice. Additionally, it uses geolocation for nearby medical facility details, streamlining guidance and improving healthcare accessibility for informed well-being.

How we built it

We built it by using the SVM ML model to predict the disease by using the accuracy and comparison technique of a double decision tree classifier.

Challenges we ran into

We ran into challenges by comparison techniques and accuracy of the different models and can be best by using the KNN Model as it Compares the nearby data clusters.

Accomplishments that we're proud of

We are proud that the model is working fine and predicting not the most accurate but up to accurate to predict the disease and give decisions according to the datasets provided.

What we learned

We learned the usage of different models in different data clusters and data behaviors and input data as well as the datasets.

What's Next for Disease Prediction by ML Model

The next step will be advancing to deep learning methods making the model more accurate and giving a probability of diseases that can be predicted by seeing the symptoms as specified by the user.