M.S. AAI Capstone Chronicles 2024

Figure 6 : This scatter plot displays the relationship between actual and predicted HbA1c values for the LSTM model. The red dashed line represents the ideal line of equality, where predictions match the actual values. The concentration of points near the line indicates strong predictive accuracy, while slight deviations, particularly at higher HbA1c ranges, suggest minor variability in the model’s performance for extreme cases. This visualization underscores the model’s capability in accurately predicting HbA1c trends, contributing to improved long-term glycemic control.

Link to video presentation https://www.youtube.com/watch?v=GSO5DDlI6oA

Link to GitHub repository https://github.com/deeshlby/AAI_590_Group_9_Capstone

References

Acuna, E., Aparicio, R., & Palominpo, V. (2023). Analyzing the Performance of Transformers for the Prediction of the Blood Glucose Level Considering Imputation and Smoothing. Big Data Cogn Comput, 7(1), 41. DOI: 10.3390/bdcc7010041

Ando, Y., Ege, T., Cho, J., Yanai, K. (2019). DepthCalorieCam: A mobile application for volume-based food calorie estimation using depth cameras. In Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, pp. 76–81 doi:10.1145/3347448.3357172 Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F. (2015). MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. Knowledge-Based Systems 89:385-397. doi:10.1016/j.knosys.2015.07.019 Ciprich, A. (2024). A Comprehensive Guide to Choosing an Automated Insulin Delivery System: 2024 Update. https://www.t1dnutritionist.com/post/a-comprehensive-guide-to-choosing-an-automated-insulin delivery-system-2024-update

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