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Saving Lives through Early Clinical Risk Detection
Application is live at: https://495a-35-237-151-197.ngrok-free.app
The Problem: Heart failure is a leading cause of global mortality; the difference between survival and fatality often depends on how early a high-risk patient is identified. The Solution: I developed a clinical diagnostic tool that analyzes patient health markers to forecast mortality risk, assisting medical professionals in prioritizing life-saving care. Key Impacts:
Early Intervention: The model identified that follow-up time is the single most critical factor in reducing fatalities, emphasizing the need for early diagnosis and consistent monitoring.
Precision Diagnostics: By analyzing heart efficiency (ejection fraction) and chemical markers like serum creatinine, the tool provides a high-accuracy (84.49%) risk score for every patient.
Clinical Support: The system helps doctors look past "statistical flukes" by accounting for outliers in medical data, ensuring that extreme clinical cases are caught rather than ignored.
Actionable Health Insights: Demonstrated a clear link between age, heart efficiency, and chemical abundance, giving providers a data-driven framework to improve long-term patient outcomes
dashboadPythonmachinelearningData AnalysisData VisualizationMachine Learning EngineeringDash PlotlyMatplotlib
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One-Click Mode
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scikit-learn — AutoML pipeline
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fpdf2 — PDF report generation
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freelance analytics projects
Open to freelance collaborations and custom dashboard / AI reporting solutions.
This looks clean and well thought out. How long did it take you to bring everything together?
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