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Stroke Prediction(Using Deep Learning Algorithms-like neural networks for better accuracy) #151
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Congratulations, @ranamanish674zu! 🎉 Thank you for creating your issue. Your contribution is greatly appreciated and we look forward to working with you to resolve the issue. Keep up the great work! We will promptly review your changes and offer feedback. Keep up the excellent work! Kindly remember to check our contributing guidelines |
@SrijanShovit if possible assign it to me ,i want to add deep learning algorithms in stroke prediction |
Not satisfied by your Issue template answers. Answer what would like to try. Which networks? Optim? Activ functions? |
These answers just look like some AI answers. Refrain from that. |
Hello @SrijanShovit Sir, I have implemented a basic Neural Network(with an accuracy of 93%) into the Storke Prediction Dataset along with 9 other different Machine Learning Algorithms. Kindly Assign this issue to me So that I can contribute it well. |
First proceed with what has been assigned to you now. Will entertain these later. |
sir we can use - simple feed forward neural network , optimization -regularization,earylystopping.....etc, activation function-relu |
Is your feature request related to a problem? Please describe.
Stroke prediction is a significant challenge in healthcare due to the complex interplay of various risk factors such as age, hypertension, diabetes, and lifestyle choices. Early and accurate prediction is crucial for timely intervention, which can greatly reduce morbidity and mortality. Traditional methods often lack precision and fail to integrate the vast amount of patient data available. This creates a need for advanced, data-driven approaches to enhance prediction accuracy and improve patient outcomes.
Describe the solution you'd like
I want to develop a stroke prediction model by applying neural network algorithms to a comprehensive dataset of patient health records. This deep learning approach should be able to analyze various risk factors and patterns within the data to provide highly accurate predictions. By leveraging the power of neural networks, the model should improve prediction accuracy over traditional methods, enabling early detection and timely medical intervention to prevent strokes and mitigate their impact.
Describe alternatives you've considered
Several alternatives have been considered for stroke prediction, including traditional machine learning models like logistic regression and decision trees, which are simpler but may lack the complexity needed for high accuracy. Rule-based systems and existing clinical risk scores offer straightforward approaches but often fail to integrate and adapt to large datasets. Ensemble methods, which combine multiple models, can improve accuracy but are more complex to implement and may not match the performance of a well-designed neural network.
Additional context
Thank you
Code of Conduct
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