# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 1 Introduction  
Diabetes mellitus is a metabolic disorder of multiple aetiologies characterized by chronic hyperglyaemia with disturbances of carbohydrate, fat, and protein metabolism resulting from defects in insulin secretion, insulin action, or both [1]. The original damage caused by diabetes mellitus is due to all kinds of diabetic complications, such as diabetic nephropathy, which is one of the most harmful. The World Health Organization (WHO) estimates that more than 180 million people worldwide have diabetes and this number is likely to be more than 350 million by 2030 [2]. Blood Glucose Measurement technology is necessary for people whose blood glucose is unbalanced to control their glucose level. According to recent researches, continuous glucose monitors (CGM) can greatly improve control of blood glucose [3]. [PERSON] has pointed out that a blood glucose measurement with an error of less than 10% every 5 min can significantly reduce the harm caused by hyperglycemia or hypoglyemia [4]. However, most of the blood glucose meters currently available on the market are invasive, which needs to puncture the fingers to collect blood as a measurement sample. This measurement method will cause pain to the patient and is particularly not convenient for young children, which can lead to inefficient self-monitoring and is not suitable for continuous glucose monitoring. In order to reduce the trauma to patients during blood glucose measurement, and increase the frequency of glucose monitoring, some medical device companies began to study mini-invasive or non-invasive blood glucose meters. In recent years, some devices have even obtained CommunateEuroppene (CE) or U.S. Food and Drug Administration (FDA) certification. The most famous product is Abbott _FreeStyle1\({}^{\text{TM}}\) Libre_[5; 6], a mini-invasive blood glucose meter attached to the patient's arm, which was approved by FDA in September 2017. Although _FreeStyle1\({}^{\text{TM}}\)_ can provide 24-h continuous blood glucose monitoring, it still needs to use a micro-needle to puncture the skin, and long-term wear can easily lead to wound infection. Not only that, _FreeStyle1\({}^{\text{TM}}\)_ needs to be replaced every 14 days, making the measurement cost too large. In addition, this mini-invasive glucose meter measures the glucose concentrations of the subcutaneous interstitial fluid, instead of blood. Numerous studies have pointed out that there is a time lag between the glucose concentration in the blood and that in the interstitial fluid [7; 8], which leads to a lower accuracy in blood glucose fluctuations or hypoglycemia, making it difficult to promptly alert. For these reasons, the research of non-invasive glucose measurement (NGM) technology has become one of the most popular topic in the medical treatment and the sensor field. The history of NGM research has been more than 30 years, but the methods of NGM are still not systemic. Fortunately, many technologies have been applied to make the measurement more accurate. The most common ways of NGM are optical, thermodynamic, and other chemical or physical methods [9]. Although there is still no method whose accuracy can satisfy the requirements of clinic application, progresses on some important aspects have been made, e.g., the relation between glucose concentration and Raman spectroscopy acquired from physical tissue has been proved in [10]. The major techniques of optical methods are based on spectrum analysis of subcutaneous structures in the near or mid infrared (0.8-2.5 \(\upmu\)m), in which the absorbance of the glucose bonds (\(C-H,O-H\)) is strong [11]. With the development of artificial intelligence, there are many researches that combines physical and physiological data with machine learning algorithm to predict glucose levels [12]. In Table 1 and Section 2, we will detail and analyze the mini-invasive and non-invasive blood glucose meters currently available on the market. Overcoming Individual Discrepancies (IDs) is one of the key technical issues in NGM by spectrum analysis methods.