Judul: The Calibration Model for Predicting Non- Invasive Blood Glucose Levels by Using the Principal Component of Quantile Regression
Abstrak: Diabetes mellitus (DM), a chronic metabolic disorder, is caused by pancreas inability to produce enough insulin (a hormone that regulates blood glucose) or of which the body cannot use the insulin produced effectively. As a result, there is an increase in the concentration of glucose in the blood (hyperglycemia). If it is not immediately prevented or addressed, DM will induce complications leading to death. DM is one of the global health problems with the fastest-growing emergency in the 21st century, so it is necessary to take preventive actions. Including checking blood glucose levels regularly. Furthermore, blood glucose level test is usually conducted in invasive ways such as by using injection and a glucometer which may injure the body. This method is takes times and spends a big expense. Based on those phenomena, the IPB bio marking team was motivated to develop a noninvasive blood glucose monitoring device that is non-injurious for the body. The development of this blood test requires calibration analysis model. The previous research had shown that there were outlier data on the results of invasive blood glucose levels measurement. However, in the study the outlier data were omitted. The outlier data in this case cannot be simply omitted, because there was suspicion of which a respondent has a blood glucose level that is extremely higher than the normal blood glucose level. The quantile regression method was applied in this case, since the method is robust to outliers and does not need a normal distribution assumption. However, the calibration modeling requires a variable reduction in advance because in general, the calibration data is highly multi collinear in explanatory variables. The method that can be used to solve this problem is the principal component analysis. This study aimed to build a calibration model for predicting non-invasive blood glucose level by using the principal component of Quantile Regression method. The response variable (Y) was the result of invasive measurements in the form of blood glucose levels (mg/dL). The explanatory variable (X) was the result of non – invasive measurement in the form of a spectrum of residual light intensity toward the time domain. This study applied 2017 and 2019 data with different tool designs. In 2017, the design of the tool with the light captured by the sensor was the light transmitted/passed by the bloods. In 2019, the tool designed sensor which captured the light reflected by the bloods. Prior to modeling, data pre-processing was carried out by two approaches. First, the approach was to summarize the area under the residual intensity curve for each periode time (period area). Second, the approach was to summarize the area under the residual intensity curve for each the lamps were on (peak area). The model was constructed by various quantiles and several numbers of principal components which were selected based on various proportions of the cumulative variance. Based on our analysis, overall RMSEP value from summarizing period area data were steadier compared to summarizing of peak area. The sixth quantile had RMSEP value of 0,0184, which was the best in predicting blood glucose level tested
Keyword: