Modelling Monthly Mental Sickness Cases Using Principal Component Regression Method

Abstract

This study was carried out to solve the problem of inadequate data information on the monthly mental sickness cases at the Federal Neuropsychiatric Hospital, Kaduna .This research tackled this problem by deriving a model from the data obtained that can be used to predict Monthly Total Observation of mental illness that will enhance effective mental health management, logistic planning and assist in decision making process. The methodology was principal component analysis (PCA) using data obtained from the hospital to estimate regression coefficients and parameters. It was found that the principal component regression model that was derived was good predictive tool. The principal component regression model obtained was okay and this was corroborated by large coefficient of determination (R²),predictive power and forecast results.

Keywords: Principal component analysis, mental illness, factor loading, eigenvalue, eigevector regression, forecasting, variance inflation factor