AI-Driven Glucose Level Estimation: A Data Science Approach (EE461-Project)

  Blog Update 3 Week 12 and 13 20/05/25 - 03/06/25 Inference Pipeline Development and Final Presentation Preparation This week marked the transition from model experimentation to a fully functional inference pipeline, laying the groundwork for real-time glucose prediction. Alongside technical implementation, we also finalized the presentation materials and prepared a demo package for showcasing the end-to-end system. Inference Pipeline Implementation To simulate real-world prediction scenarios, a test sample was extracted and pushed to the end of the dataset. This allowed us to mimic unseen data inference while preserving a ground truth value for validation. The complete dataset, including this test sample, was then passed through the full preprocessing workflow, which included normalization and dimensionality reduction. Curvilinear Component Analysis (CCA) was used to transform the feature space, reducing it to 45 dimensions. This technique, already proven effective in prio...

  Blog Update 2 Weeks 9 to 11 – 28/04/2025 to 16/05/2025 Feature Selection and Early Model Testing Feature Diagnostics and Ranking Using MATLAB’s Feature Diagnostic Designer , we calculated, ranked, and extracted the most relevant features for glucose estimation. These features were then applied across various quick-to-train machine learning models to measure performance improvements. The best-performing model achieved an R² value of 0.14 , marking a 14% improvement over the previously implemented neural network model. This result was obtained using voltage sensor data , reinforcing the value of sensor-based inputs in glucose prediction. ANN Prototyping and Evaluation An Artificial Neural Network (ANN) was designed and tested using lifestyle data . The model achieved an accuracy of 55% on both validation and test sets. While modest, these results demonstrate potential for model development, especially in scenarios where only lifestyle indicators are available. As...

  Blog Update 1 Week 6 – 31/03/2025 Blog Creation and Backup The project blog was officially created to serve as a central repository for progress tracking, updates, and backup on GitHub. Relevant files were uploaded for safekeeping and version control, establishing a workflow for consistent documentation and data management. Week 7 – 24/04/2025 Part 1 Feature Engineering and Initial Exploration The analysis began with the selection and visualization of five engineered features. Using MATLAB, basic exploratory techniques such as box plots and scatter plots were employed to gain an initial understanding of data distributions and the relationships between predictors and the target variable. Principal Component Analysis (PCA) PCA was applied to both unnormalized and normalized data to assess the variance captured by each principal component. Pareto charts were used to visualize component significance, while 2D and 3D scatter plots facilitated the identification of patter...

  EE461 Project Blog: Glucose Estimation Model Development Welcome to the official blog for our EE461 project! This space will serve as a central hub for logging our progress, sharing insights, backing up key developments, and tracking milestones throughout the project. Posts will be updated fortnightly, ensuring consistent documentation of our journey as we work towards building a robust glucose estimation model. Project Overview The primary goal of this project is to develop a reliable machine learning model capable of estimating glucose levels based on available physiological or sensor data. This task involves building the model itself and ensuring the quality and relevance of the input data, engineering meaningful features, and validating model performance through sound evaluation metrics. Project Objectives To achieve our goals, the project will be guided by the following key objectives: Data Preprocessing Cleaning and preparing the raw dataset is a crucial first ste...