q-dx (hybrid quantum machine learning platform for cardiovascular risk assesment

The Problem It Solves

Limitations of Traditional Diagnostics:tandard machine learning models often struggle to capture complex, non-linear hidden risk patterns present in clinical cardiovascular data.

Delayed Risk Detection: Cardiovascular diseases remain a leading global health threat, and traditional diagnostic methods can sometimes miss early-stage risk indicators.
Lack of Quantum Integration in Healthcare:Existing medical systems lack a seamless platform combining quantum computing capabilities with classical machine learning to deliver high-accuracy consensus metrics.

How Q-Dx Solves It

Hybrid Quantum-Classical Architecture :Integrates classical Random Forest models with Quantum Machine Learning (Qiskit VQC/QSVC via Aer Simulator) to achieve higher accuracy (89.2%) and diagnostic precision.

Interactive Diagnostic Dashboard:Provides a real-time web platform featuring patient assessment, risk results, prevention guidelines, and head-to-head performance metrics based on the Cleveland dataset (303 clinical cases).

Multi-Language PDF Report Generation:Instantly converts complete patient assessment reports into downloadable PDF documents across multiple languages, ensuring accessibility and ease of understanding for any patient or medical practitioner.