Stroke Prediction

1. IntroductionStroke Predictor is a sophisticated web application designed to assess an individual's risk of hypertension. Utilizing machine learning techniques, the application processes various user-inputted factors such as age, blood pressure readings, presence of hypertension, gender, and smoking status. The system's predictive model evaluates this data to provide real-time risk assessments, empowering users to make informed health decisions.

2. Project ObjectivesRisk Prediction: Develop a machine learning model capable of accurately predicting the risk of hypertension based on diverse user data.

User-Friendly Interface: Design an intuitive and accessible user interface allowing users to input their data effortlessly.

Real-Time Results: Implement a system that delivers instant predictions, ensuring a seamless user experience.

3. MethodologyData Collection: Curate a comprehensive dataset encompassing various factors related to hypertension, including demographic details, blood pressure values, lifestyle choices, and existing health conditions.

Feature Engineering: Identify relevant features and preprocess the dataset to prepare it for model training.

Model Selection and Training: Choose an appropriate machine learning algorithm (e.g., logistic regression, random forest) for classification. Train the model using the processed dataset, optimizing its performance through iterative refinement.

Web Development: Build an interactive web interface using modern web technologies (HTML, CSS, JavaScript) and integrate the trained machine learning model to provide real-time predictions.

4. Implementation DetailsUser Interface: The application boasts an intuitive design, allowing users to enter their data seamlessly. Clear input fields guide users to provide accurate information.

Machine Learning Model: Employ a trained classification model that evaluates the user's input based on the dataset's features. The model processes this information and promptly delivers the risk prediction.

Scalability: Design the application with scalability in mind, enabling it to handle a substantial user base efficiently.

5. Results and BenefitsThe Stroke Predictor application successfully provides real-time risk assessments to users. Its accuracy and efficiency empower individuals to understand their potential risk factors, facilitating early intervention and healthier lifestyle choices. By providing personalized insights, the application promotes preventive healthcare.

6. Challenges and Future EnhancementsData Quality: Ensuring the dataset's accuracy and relevance posed challenges, which were mitigated through rigorous preprocessing.

User Education: Addressing user misconceptions about hypertension and its risk factors through informative content within the application.

Future Enhancements: Incorporate additional features such as dietary habits, exercise routines, and stress levels for more comprehensive risk assessments. Implement user accounts for personalized health tracking and recommendations.

7. ConclusionThe Stroke Predictor web application stands at the intersection of healthcare and technology, providing a valuable tool for users to assess their hypertension risk. By leveraging machine learning and an intuitive interface, the project exemplifies the potential of technology in promoting proactive health management. Its real-time predictions and user-focused design make it a significant contribution to preventive healthcare initiatives. With ongoing enhancements, Stroke Predictor is poised to continue making a positive impact on public health and awareness.

You can fint the project along with the source code here:- https://github.com/roshni-1/stroke_prediction