Heart-Stroke-Prediction

This data science project aims to predict the likelihood of a patient experiencing a stroke based on various input parameters such as gender, age, presence of diseases and smoking status. The dataset provides relevant information about each patient, enabling the development of a predictive model.

According to the World Health Organization (WHO), stroke is the second leading cause of death worldwide, responsible for approximately 11% of total deaths. This project aims to leverage machine learning techniques to build a predictive model that can identify individuals at risk of stroke based on their demographic and health-related features. By detecting high-risk individuals early, appropriate preventive measures can be taken to reduce the incidence and impact of stroke.

About the dataset

The dataset used in this project contains information necessary to predict the occurrence of a stroke. Each row in the dataset represents a patient, and the dataset includes the following attributes:

• id: Unique identifier
• gender: "Male", "Female" or "Other"
• age: Age of the patient
• hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
• heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease
• ever_married: "No" or "Yes"
• work_type: "Children", "Govt_job", "Never_worked", "Private" or "Self-employed"
• Residence_type: "Rural" or "Urban"
• avg_glucose_level: Average glucose level in the blood
• bmi: Body mass index
• smoking_status: "Formerly smoked", "Never smoked", "Smokes" or "Unknown"
• stroke: 1 if the patient had a stroke, 0 if not

Data Visualisation

Running the Streamlit App

This project includes an interactive web app for predicting stroke risk using a trained machine learning model.

Requirements

Make sure you have installed the required packages:

pip install -r requirements.txt

Running the App

streamlit run app.py

App is available at https://omari-heartstrokeprediction.streamlit.app/.

Tools & Technologies

• Python: Core language for data analysis and modeling
  • pandas, NumPy — for data manipulation and numerical operations
  • Matplotlib, Seaborn — for data visualization
  • scikit-learn — for machine learning modeling and evaluation
• Jupyter Notebook: For interactive development, analysis and documentation of the data science workflow
• Streamlit: For deploying the model as a simple web app
• VS Code: Code editing and project management
• Git & GitHub: Version control and collaboration

Contribution

Open to suggestions and improvements! Submit issues or pull requests.