Heart failure

[abhisek2004]

🔍 Problem Description: Heart failure is a chronic illness affecting millions globally, leading to increased morbidity, mortality, and healthcare costs. Early detection and effective management are crucial for improving patient outcomes and alleviating strain on healthcare systems. Predictive modeling utilizing advanced statistical and machine learning techniques can help identify individuals at high risk for heart failure, enabling timely interventions and tailored therapies.

🧠 Model Description: We will develop predictive models using historical patient data, including demographics, clinical features, test results, and medical history. The primary algorithm will be Random Forest, which has demonstrated strong performance with an accuracy of 0.93 in previous tests. We will also explore other machine learning methods, including Neural Networks, to compare effectiveness. The model will be validated using appropriate metrics to assess its predictive power, accuracy, precision, and recall.

⏲️ Estimated Time for Completion: The estimated time to complete the project, including data gathering, preprocessing, model training, evaluation, and validation, is approximately 4-6 weeks.

🎯 Expected Outcome: The expected outcomes include:

• Accurate Predictive Models: Development of effective models capable of predicting the likelihood of heart failure, facilitating early interventions.
• Improved Clinical Decision-Making: Tools for healthcare practitioners to optimize patient management, leading to better patient outcomes.
• Personalized Care: Treatment plans tailored to individual risk profiles, reducing the incidence of heart failure and its complications.
• Healthcare Efficiency: Enhanced resource allocation and reduced healthcare expenditures through early detection and intervention.

📄 Additional Context: This project will utilize a dataset comprising clinical features, medical histories, test results, and patient demographics. The development environment will include Python IDEs (e.g., VS Code or Google Colab) with libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. The final model will be implemented through a Streamlit application, allowing healthcare professionals to input patient data and receive real-time risk assessments. Future enhancements will focus on expanding the dataset and integrating real-time data from multiple hospitals to improve accuracy and clinical applicability.

To be Mentioned while taking the issue:

• Participant role: GSSOC Participant

Note:

• I will review the project documentation and ensure that my code aligns with the project structure.
• The predict.py file will include a properly implemented model_details() function to print a detailed model report.
• I will create a new branch for this work to maintain the stability of the main branch.
• I will strictly follow the provided pull request template when submitting my changes.

[abhisek2004]

Request for Review of Upcoming Pull Request

Dear Project Admin,

I hope this message finds you well. I, Abhisek Panda, am currently working on developing predictive models for heart failure, as outlined in my previous issue submission. I plan to complete this work within the next two days.

Once I finish, I will create a new pull request for your review. I would greatly appreciate it if you could verify and accept it at your earliest convenience.

Thank you if you support me!

Best regards,
Abhisek Panda