Kamal Muhamed
Detecting Heart Stroke in patients is difficult using traditional methods. We are solving this by making the detection of stroke in a patient automated/semi-automated using advanced technology by studying the outlining patterns associated with people who are known to have stroke.
The dataset used in this project is the stroke-data.csv
To run this project, you will need to have the following software installed on your system:
Python 3
Google Colab
Required packages - pandas, numpy, scikit-learn, seaborn, matplotlib
Here are some of the insights after our analysis
I used a KNN to make the Stroke predictions.
Our model peformed amazingly having a recall of 100% meaning that our model can predict 100% of the time patients with stroke. It also has a precision of 100% meaning our model can predict 100% of the time patients that don't have stroke
I have deployed this model and turned it into an interactive web app using Streamlit. Interact with the web app by clicking this link: https://kamal-moha-stroke-prediction-app-r89nxn.streamlit.app/
Limited feature set: This dataset was very imbalanced with over 95% of the patients not having stroke. This created bias as our model would learn more of one side. But we solved this by oversampling the positive class.
Take it to the Real World: We need to use our model to make predictions using unseen data to see how it performs
Incorporate more data: To improve our dataset in the next iterations, we need to include more data points of people with stroke so that we can create target balance before modeling
Scikit-learn for the machine learning library.
Matplotlib and Seaborn for data visualization.
For any additional questions, please email **Kamalmohapy@gmail.com