# MOBKA

**Repository Path**: Tomhappy/MOBKA

## Basic Information

- **Project Name**: MOBKA
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-11
- **Last Updated**: 2026-01-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Development, Simplification, Validation, and Decision Support System of Chinese Endoscopy Patient Experience Scale

This repository contains the code and data associated with the research project titled "Development, Simplification, Validation, and Decision Support System of Chinese Endoscopy Patient Experience Scale." The project involves developing a patient experience scale for endoscopy procedures, simplifying it using advanced algorithms, validating the simplified scale, and implementing a decision support system based on the scale.
## Repository Contents

The repository is organized into the following folders:
### 1. Comparison of Improved BKA Swarm Intelligence Algorithm

Folder Name: Comparison of Improved BKA Swarm Intelligence Algorithm

This folder contains the code for the improved BKA  swarm intelligence algorithm and its simulation tests. It includes comparisons with four other algorithms to evaluate performance improvements.
Contents:

    Code Implementation: Source code implementing the improved BKA algorithm.
    Simulation Tests: Scripts and notebooks used to compare the improved BKA algorithm with other swarm intelligence algorithms.
    Results: Output files and figures demonstrating the performance comparison.

### 2. Scale Simplification

Folder Name: Scale simplification

This folder contains the code used for simplifying the patient experience scale utilizing the MOBKA (Multi-Objective Bat K-means Algorithm). The goal is to reduce the number of items on the scale while maintaining its validity and reliability.
Contents:

    Simplification Code: Source code implementing the MOBKA algorithm for scale simplification.
    Documentation: Detailed instructions on how to run the code and interpret the results.

### 3. Decision Support System

Folder Name: Decision Support System

This folder includes the code and resources for developing the decision support system based on the simplified patient experience scale. The system aims to assist healthcare providers in making informed decisions by analyzing patient experience data.
Contents:

    Application Code: Source code for the decision support system application.
    User Interface: Files related to the graphical user interface or web interface of the system.
    Deployment Instructions: Guidelines on how to set up and run the decision support system.
    User Manual: Instructions for end-users on how to use the system effectively.

### 4. Raw Data

Folder Name: Raw data

This folder contains the original data collected during the research study. It includes two Excel files with patient data from endoscopy procedures.
Contents:

    ColonData.xlsx: Raw data collected from patients undergoing colonoscopy procedures.
    GastroscopyData.xlsx: Raw data collected from patients undergoing gastroscopy procedures.

Note: The raw data is provided for replication and validation purposes. Please ensure compliance with all relevant data protection regulations and ethical guidelines when accessing and using this data.

## Project Overview

The project encompasses the following key objectives:

    . Development of Patient Experience Scale: Creating a comprehensive scale to measure the experiences of Chinese patients undergoing endoscopy.
    . Simplification Using MOBKA Algorithm: Employing the Multi-Objective Bat K-means Algorithm to simplify the scale without compromising its effectiveness.
    . Validation of Simplified Scale: Testing the reliability and validity of the simplified scale through statistical analysis.
    . Implementation of Decision Support System: Developing a software system to aid healthcare professionals in interpreting patient experience data and improving clinical outcomes.