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  Home Page

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  Prediction results

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  Blog on heart attack and its symptoms

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  Treatment Suggestions

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  Suggestion for Change in lifestyle

About the Project: HeartCare

Inspiration

Heart disease remains one of the leading causes of death worldwide, and timely diagnosis is crucial for saving lives. However, many patients face challenges accessing medical facilities due to long travel distances, especially in remote or underserved areas. This inspired us to develop HeartCare, a web-based tool to bring heart disease diagnosis closer to patients, enabling them to receive essential insights without the need for frequent travel.

What We Learned

Throughout this journey, we gained a deeper understanding of:

• The importance of accessible healthcare solutions in improving patient outcomes.
• Machine learning techniques and their application in predicting cardiovascular diseases (CVD).
• The intricacies of integrating medical datasets with technology to build reliable prediction models.

How We Built the Project

1. Dataset:
   We utilized a publicly available dataset containing key health metrics such as age, blood pressure, cholesterol levels, and other cardiovascular indicators.

2. Machine Learning Model:

   • We implemented various algorithms like Random Forest and Stacked Models to achieve accurate predictions.
   • Ensemble techniques were applied to optimize the model, improving accuracy to 88%.

3. Web Development:

   • Built an intuitive and user-friendly interface for patients and healthcare providers.
   • Integrated the machine learning model into a web application, enabling real-time diagnosis.

4. Tools and Technologies:

   • Python (for data preprocessing and model development).
   • Flask/Django for the web backend.
   • HTML, CSS, and JavaScript for a responsive frontend.

Challenges We Faced

• Data Preprocessing: Cleaning and preparing the dataset to ensure accurate model training.
• Model Optimization: Achieving a balance between model accuracy and computational efficiency.
• Accessibility: Designing a web interface that is simple and intuitive for users with minimal technical knowledge.

Conclusion

HeartCare is a step toward bridging the gap between patients and timely heart disease diagnosis. By leveraging web technology and machine learning, we aim to empower individuals to take control of their heart health without unnecessary travel, making healthcare more accessible and impactful.

Built With

• bootstrap
• css
• flask
• html5
• keras
• python
• scikit-learn
• tensorflow