# Application of Improved LightGBM Model in Blood Glucose Prediction
## 1 Introduction  
The recent years have seen a rapid increase in the incidence of diabetes around the world, due to many factors such as the continuous improvement in people's living standards, changes in dietary structure, an increasingly rapid pace of life, and a sedentary lifestyle. Diabetes has become the third major chronic disease that seriously threatens human health, following cancer and cardiovascular disease [1, 2]. According to statistics from the International Diabetes Federation (IDF), there were approximately 425 million patients with diabetes across the world in 2017. One in every 11 adults has diabetes, and one in every two patients is undiagnosed [3]. As of 2016, diabetes directly caused 1.6 million deaths [4], and it is estimated that by 2045, nearly 700 million people worldwide will suffer from diabetes, which will pose an increasing economic burden on health systems in most of countries. It is forecast that by 2030, at least $490 billion will be spent on diabetes globally [5]. Currently, the diagnosis rate of diabetes is low, as patients exhibit no obvious symptoms in the early onset of the disease, and many people do not realize they have the disease [6]; thus, early detection and diagnosis are particularly needed. It is relatively simple to measure a person's blood glucose under the existing medical conditions, but it takes a lot of human and material resources to detect the blood glucose of a large number of people in medical examination. Therefore, the predictionof the blood glucose of a large number of people in medical examination by machine learning can save a lot of unnecessary expenses (for example [7]). With the application of machine learning in medical fields, more and more people are applying emerging prediction methods to many different medical fields to help greatly reduce the workload of the related medical staff and improve the diagnosis efficiency of doctors. For example, [PERSON] and [PERSON] applied the XGBoost model for the early diagnosis of lung cancer [8]. [PERSON] used the XGBoost model to predict and analyze colorectal cancer in Indonesia [9]. [PERSON] forecasted the prognosis of hypertension using the XGBoost model [10]. [PERSON] applied the XGBoost model for the diagnosis of chronic kidney disease [11]. [PERSON] used the XGBoost model and clustering algorithm to analyze the probability of hypertension-related symptoms [12]. [PERSON] predicted severe hand, foot, and mouth disease using the CatBoost model [13]. As the XGBoost model generates a decision tree using the level-wise method [14], it the splits leaves simultaneously on the same layer, but the splitting gain of most leaf nodes is low. In many cases where further splitting is unnecessary, the XGBoost model will continue to split, causing non-essential expenses. Compared with the LightGBM model, the XGBoost model occupies more memory and consumes more time when the dataset is large. The traditional genetic algorithm and random search algorithm have different defects--the genetic algorithm is a natural adaptive optimization method that simulates the problem to be solved as a process of biological evolution, and gradually eliminates the solutions with low fitness function values through generating next-generation solutions by operations such as replication, crossover, and mutation [15]. The genetic algorithm uses the search information of multiple search points at the same time, and adopts probabilistic search technology to obtain the optimal or sub-optimal solution of the optimization problem. It boasts of good search flexibility, global search capability, and is easily implemented [16]. However, it has a poor local search ability, complicated process caused by many control variables, and there are no definite termination rules. The random searching algorithm (RandomizedSearchCV) performs a random search in a set parameter search space. It samples a fixed number of parameters from a specified distribution instead of trying all of the parameter values [17].