Diabetes Prediction 1 0

Diabetes prediction V1.0 uses the Markov Chains method. First, this VB6 application converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. Next, the transition matrix is further used for a prediction in a Markov chain.

AI & Machine LearningVisual Basic 6.0MIT

Abstract

Diabetes Prediction 1 0 is an open-source AI & Machine Learning project. Diabetes prediction V1.0 uses the Markov Chains method. First, this VB6 application converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. Next, the transition matrix is further used for a prediction in a Markov chain. The above sequence represents the glycemic values from a single individual who does not have diabetes, but a family predisposition for diabetes. Each number in the sequence represents a day. It is built using Visual Basic 6.0, Machine Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

The above sequence represents the glycemic values from a single individual who does not have diabetes, but a family predisposition for diabetes. Each number in the sequence represents a day. Thus, the sequence contains observations that extend over 42 days.

2. Objective

Diabetes prediction V1.0 uses the Markov Chains method. First, this VB6 application converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. Next, the transition matrix is further used for a prediction in a Markov chain.

This project demonstrates how Visual Basic 6.0, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Visual Basic 6.0Machine Learning

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • See the project README for exact requirements
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Gagniuc/Diabetes-prediction-1.0.git
cd Diabetes-prediction-1.0

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Gagniuc and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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