Abstract
Diabetes Prediction 2 0 is an open-source AI & Machine Learning project. Diabetes prediction V2.0 -This VB6 application takes glycemic values and tries to predict the future state of the patient. First, it converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. The transition matrix is further used for a predictions. The application Diabetes prediction prototype is an experimental software intended as a method of prediction for type II diabetes. Examples for this program are related to glycemic values. It is built using Visual Basic 6.0. 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 application Diabetes prediction prototype is an experimental software intended as a method of prediction for type II diabetes. Examples for this program are related to glycemic values. Note that these glycemic values are not binding. Other numeric values (integers originating from any type of biochemical data) can also be used in different contexts. However, this application converts a sequence of numbers into a sequence of two states (strings like ABABBBBABBBBABBA). The sequence of states is then converted into a transition matrix. Transition probabilities are calculated for each element of the transition matrix based on the sequence of states. The transition matrix is further used for a prediction in a Markov chain.
a) Patient blood sugar for a period of time (days) Info: the main input of the program.
b) Processes k steps Info: the period of time for which the prediction is made. If the series of blood glucose levels from the input is composed of blood glucose values measured once a day, then the predictions are made on days. If, for instance, the input consists of blood glucose values measured once a week, then the predictions are made on weeks.
2. Objective
Diabetes prediction V2.0 -This VB6 application takes glycemic values and tries to predict the future state of the patient. First, it converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. The transition matrix is further used for a predictions.
This project demonstrates how Visual Basic 6.0 can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
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-2.0.git
cd Diabetes-prediction-2.0Full 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.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- 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.
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