Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient’s glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention.

中文翻译：

---

优化 2 型糖尿病管理：人工智能增强的连续血糖监测数据时间序列分析，用于个性化饮食干预

尽管许多发达国家的卫生设施先进，但糖尿病患者仍面临多重健康挑战。根据美国糖尿病协会 (ADA) 的数据，2 型糖尿病 (T2DM) 会因频繁出现峰值、低血糖 <=70 mg/dL（空腹时）或餐后两小时高血糖 >=180 mg/dL 而伴有明显症状）。 2型糖尿病的严重后果恰恰与患者不良的生活方式有关。尤其是，健康的饮食和营养的食物是此类患者成功的关键。这项研究旨在帮助 T2DM 患者在人工智能辅助的连续血糖监测 (CGM) 数字系统下养成良好的生活方式，从而改善健康状况。本研究旨在通过纠正此类患者的日常饮食并保持其运动与食物消耗记录来减少此类患者的血糖水平波动。在这项研究中，通过在观察的 T2DM 患者身上连接的 CGM 传感器设备记录的数据集上训练 ML 模型，获得了非常精确的预测。由于CGM传感器获取的数据是时间序列，为了预测血糖水平，使用XGBoost、SARIMA和Prophet进行时间序列分析和预测。然后根据性能指标比较不同模型的结果。这有助于监测各种趋势，特别是 CGM 传感器收集的患者血糖数据的不规则模式。随后，通过跟踪这些趋势和季节性，通过添加或删除特定食物并通过市售一体式食品识别人工智能解决方案的干预来跟踪其营养成分，从而相应地调整饮食。这就创建了一个交互式辅助系统，将预测结果与食物含量进行比较，以使血糖水平处于正常范围内，以保持健康的生活方式，并在即将发生的时间之前提醒血糖波动。这项研究将帮助 T2DM 患者控制糖尿病，并最终在干预后三个月将 HbA1c 控制在糖尿病和糖尿病前期患者的正常范围内 (<= 5.7%)。