LDL-Cholesterol Levels Measurement Using Hybrid Genetic Algorithm and Multiple Linear Regression

Cholesterol level is the significant factor which causes cardiovascular disease. The cholesterol types used to measure the fat level are total, low density lipoprotein (LDL), high density lipoprotein (HDL) and Triglycerides (TG). There are two methods used to measure the cholesterol level. The first method is by directly measuring the patient blood which although yields the best accuracy, is accompanied by the high cost. The second method is the calculation method, which has a lower cost, and a lower accuracy. High levels of LDL cholesterol are important factor that increase the risk for patients to acquire the disease. The cost for the high accuracy of LDL cholesterol levels detection is expensive. In order to decrease the overall cost, the detection process using the calculation method requires improvement of accuracy which could then justify a change to use this method. This study presents the combination methods between Multiple Linear Regression (MLR) and a Hybrid Genetic Algorithm (HGA) to explore an equation that is precise and suitable to detect the LDL cholesterol. In this experiment, we compare the results from MLR-HGA technique with the other three methods, i.e. Friedewald formula (FF), MLR and Multiple Linear Regression Genetic Algorithm (MLR-GA). The findings resulted in an investigated that the MLR-HGA techniques have a higher accuracy than the results from other three methods.