This project aims to predict chronic liver disease using an ensemble quantum approach, combining Quantum Machine Learning (QML) and Classical Machine Learning (CML) techniques. The study leverages the power of two Quantum Machine Learning models, Variational Quantum Classifier (VQC) using IBM Qiskit, and Quantum Neural Network (QNN) using AWS Braket. Additionally, four classical machine learning models are employed: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest Classifier (RFC), and Artificial Neural Network (ANN). A comparative analysis between quantum and classical models is performed, and an ensemble quantum approach is proposed by utilizing the RFC model with VQC using Qiskit.
- Project Documentation: https://drive.google.com/file/d/1-PlwtvclLvavKYYehg7V-ELt9LkZmuhr/view?usp=sharing
- IBM Qiskit for providing the VQC model implementation.
- AWS Braket for providing the QNN model implementation.
- Various Python libraries used in this project for data processing and model development.