# Application of Improved LightGBM Model in Blood Glucose Prediction
## Materials and Methods
### Data Preprocessing  
The dataset in this paper is the diabetes data from September to October 2017 in a grade-three first-class hospital, provided by the Tianchi competition platform as the data source, with a total of 7642 pieces of data and 42 eigenvalues, As shown in Table 1. The eigenvalues include the following:  
Figure 5: Experiment flow chart. In this paper, the dataset was divided, with 6642 pieces of data as the training set, and the remaining 1000 pieces of data as the test set. \begin{table}
\begin{tabular}{c c} \hline
**Eigenvalue Name** & **Eigenvalue Name Explanation (Unit)** \\ \hline ID & Physical examination personnel ID \\ \hline Gender & Male/female \\ \hline Age & Age \\ \hline Date of physical examination & Date of physical examination \\ \hline Aspartate aminotransferase & Aspartate aminotransferase (U/L) \\ \hline Alanine aminotransferase & Alanine aminotransferase (U/L) \\ \hline Alkaline phosphatase & Alkaline phosphatase (U/L) \\ \hline R-Glutamyltransferase & R-Glutamyltransferase (U/L) \\ \hline Total protein & Total serum protein (g/L) \\ \hline Albumin & Serum albumin (g/L) \\ \hline Globulin & Globulin (g/L) \\ \hline White ball ratio & Ratio of albumin to globulin \\ \hline Triglyceride & Serum triglyceride (mmol/L) \\ \hline Total cholesterol & Total cholesterol in lipoproteins (mmol/L) \\ \hline High density lipoprotein cholesterol & High density lipoprotein cholesterol (mg/dl) \\ \hline LDL cholesterol & LDL cholesterol (mg/dl) \\ \hline Urea & Urea (mmol/L) \\ \hline Creatinine & Products of muscle metabolism in human body (\(\mu\) mol/L) \\ \hline Uric acid & Uric acid (umol/L) \\ \hline Hepatitis B surface antigen & Hepatitis B surface antigen (ng/mL) \\ \hline Hepatitis B surface antibody & Hepatitis B surface antibody (mIU/mL) \\ \hline Hepatitis B e antigen & Hepatitis B e antigen (PEI/mL) \\ \hline Hepatitis B e antibody & Hepatitis B e antibody (P/mL) \\ \hline Hepatitis B core antibody & Hepatitis B core antibody (PEI/mL) \\ \hline Leukocyte count & Leukocyte count (\(\times 10^{9}\)/L) \\ \hline RBC count & RBC count (\(\times 10^{12}\)/L) \\ \hline Hemoglobin & Hemoglobin (g/L) \\ \hline Hematocrit & Hematocrit \\ \hline Mean corpuscular volume & Mean corpuscular volume (fl) \\ \hline Mean corpuscular hemoglobin & Mean corpuscular hemoglobin (pg) \\ \hline Mean corpuscular hemoglobin concentration & Mean corpuscular hemoglobin concentration (g/L) \\ \hline Red blood cell volume distribution width & Red blood cell volume distribution width \\ \hline Platelet count & Platelet count (\(\times 10^{9}\)/L) \\ \hline Mean platelet volume & Mean platelet volume (fl) \\ \hline Platelet volume distribution width & Platelet volume distribution width (\%) \\ \hline Platelet specific volume & Platelet specific volume (\%) \\ \hline Neutrophils & Neutrophils (\%) \\ \hline Lymphocyte & Lymphocyte (\%) \\ \hline Monocyte & Monocyte (\%) \\ \hline Eosinophils & Eosinophils (\%) \\ \hline Basophilic cell & Basophilic cell (\%) \\ \hline Blood glucose & Blood glucose level (mg/dl) \\ \hline \end{tabular}
\end{table} 
Table 1: Introduction to eigenvalues. Firstly, the missing values in the original dataset were analyzed to obtain the proportion of the missing data. It can be seen from Figure 6 that the missing proportion of five basic features--hepatitis B surface antigen, hepatitis B surface antibody, hepatitis B core antibody, hepatitis B e antigen, and hepatitis B e antibody--is over 70%, significantly exceeding the missing proportion of other basic features. Therefore, these features with large missing values were deleted, and those with smaller missing values were filled with medians. Secondly, the weight of the influence of the data features was analyzed. The weight of the eigenvalues can be obtained through related functions. According to the eigenvalue weight of each item, the eigenvalues that have no effect on the results can be found. By processing the invalid eigenvalues, the accuracy of the experiment can be further improved.