Arima Model to Predict the Prevalence of Diabetes Type 1 and Type 2 Patients: A Case Study of Jos University Teaching Hospital
Termen Nanfwang Yunana *
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
K. E. Lasisi
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
A. M. kwami
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Douglas Jah Pam
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Sheyi Mafolasire
Federal College of Forestry, Jos, Nigeria.
Chibuike John Echebiri
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Friday Ezekiel Danung
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Salihu Gambo
Department of Statistics School of Technology, Kano State Polytechnic, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Diabetes Mellitus is a huge burden for human health, increasing number of patient is likely to result in rising demand for the medical emergencies. Due to limited number of hospitals with standard laboratory test kits to differentiate between type 1 and type 2 diabetes it is important to forecast the future incidences and prepare with proper resource planning. The monthly number of Diabetes patients obtained from Jos University Teaching Hospital is fitted by autoregressive integrated moving average (ARIMA) model. Dataset starting from January, 2010 to December,2020. Using ARIMA, several models were evaluated based on the Bayesian Information Criterion (BIC) and Ljung-Box Q statistics. ARIMA(3, 1, 1) is found to be better and used to describe and predict the future trends of Diabetes type 1 and ARIMA(1,1,1) is a better model to predict the future prevalence of diabetes type 2. Therefore, the proposed model will help in the appropriate planning and allocation of resources for emergencies.
Keywords: Diabetes, mellitus, hospital, ARIMA, statistics, forecasting, BIC