title of the paper: Variational Quantum Classifier for Chronic Kidney Disease Detection

Overview

This project implements a Variational Quantum Classifier (VQC) model for the early identification and classification of Chronic Kidney Disease (CKD). The approach integrates a sparse autoencoder and Lasso Shrinkage to handle feature selection and dimensionality reduction. The model leverages quantum computing techniques, encoding classical medical data into quantum states to improve classification performance.
Dataset

The dataset used in this project includes patient data relevant to the identification of chronic kidney disease, including features such as:

    Age
    Blood pressure
    Specific blood markers (e.g., creatinine, hemoglobin)
    Glomerular filtration rate (GFR)
    Other medical attributes used for diagnosing CKD

File structure of the dataset:

    chronic_kidney_disease.csv: Contains the labeled medical data for CKD classification.

Implementation

The implementation utilizes a variational quantum classifier, which is designed to handle high-dimensional medical data by leveraging quantum circuits for feature encoding and classification.
Key Components:

    Sparse Autoencoder: Used for dimensionality reduction by learning a compressed representation of the input data.
    Lasso Shrinkage: Applied to select relevant features by imposing a penalty on the regression coefficients.
    Quantum Encoder: Encodes classical data into quantum states using a predefined quantum circuit.
    Quantum Classifier: A variational quantum circuit is optimized for classifying the input data into CKD and non-CKD classes.

Main Files:

    encoder.py: Handles the quantum circuit encoding process.
    qclassifier.py: Implements the variational quantum classifier.
    training.py: Manages the training loop for optimizing the quantum model using cross-entropy loss.
    xor_example.py: Example quantum data generation and encoding process.
    preprocessing.py: Preprocessing functions for feature scaling and transformation.

Dependencies

To run the code, the following dependencies are required:

    qiskit: A framework for simulating and running quantum circuits.
    numpy: For numerical operations.
    sklearn: For preprocessing and classical methods like Lasso.
    matplotlib: For visualizations.

Install the dependencies using the following command:

pip install qiskit numpy sklearn matplotlib

Running the Code

    Prepare the dataset: Place the CKD dataset in the working directory.

    Train the quantum classifier:

python train.py

This will train the VQC model on the CKD dataset and output classification metrics.

Evaluate the model:

    python evaluate.py

    This will generate performance metrics, including accuracy, precision, and recall.

Results

The quantum classifier is evaluated based on its ability to classify chronic kidney disease with a high degree of accuracy. The implementation demonstrates the potential for quantum machine learning models to outperform classical methods on high-dimensional medical datasets.
Future Work

Future directions could explore enhancing the quantum circuits for larger datasets, optimizing encoding schemes, and applying the approach to other medical conditions.