Application of Principal Component Analysis (PCA) to Medical Data

Objectives: To apply Principal Component Analysis to medical data to explore the factors thought to be very important in increasing the risk of Ischemic Heart Diseases. Methods/Statistical Analysis: PCA was performed in R-mode using correlation and covariance for medical data. Variables pertaining to chemical tests of blood namely cholesterol, high density lipoprotein, triglyceride, Apo protein A-1, Apo protein B, low density lipoprotein, phospholipids, total lipid, glucose and uric acid, are undertaken to know the relationship between them and membership of group variable. Findings: The results indicated that among these factors cholesterol, triglyceride, Apo protein B, low density lipoprotein, phospholipids, total lipid, and uric acid were recorded to be higher in IHD group compared to those of control group. High density lipoprotein and Apo protein A-1 were recorded to be lower in IHD group, whereas found higher in control group. Cholesterol was highly correlated with low density lipoprotein and moderately correlated with total lipid. Cholesterol, Apo protein B and low density lipoprotein belonged to component 1, Apo protein A-1, phospholipids and uric acid belonged to component 2, triglycerides and total lipid belonged to component 3 and high density lipoprotein and glucose belonged to component 4. The first four components have explained 60.67 percent component variability. Improvements/Applications: The end results showed that the average cholesterol level, which is considered as the main risk factor of Ischemic Heart Disease, was found higher even in control group.