Interrelated Decision-Making Model for Diabetes

Normadiah Mahiddin, Zulaiha Ali Othman, Nur Arzuar Abdul Rahim
dx.doi.org/10.17576/apjitm-2021-1002-12

Abstract

Diabetes is one of the growing chronic diseases. Proper treatment is needed to produce its effects. Past studies have proposed an Interrelated Decision-making Model (IDM) as an intelligent decision support system (IDSS) solution for healthcare. This model can provide accurate results in determining the treatment of a particular patient. Therefore, the purpose of this study is to develop a diabetic IDM to see the increased decision-making accuracy with the IDM concept. The IDM concept allows the amount of data to increase with the addition of data records at the same level of care, and the addition of data records and attributes from the previous or subsequent levels of care. The more data or information, the more accurate a decision can be made. Data were developed to make diagnostic predictions for each stage of care in the development of type 2 diabetes. The development of data for each stage of care was confirmed by specialists. However, the experiments were performed using simulation data for two stages of care only. Four data sets of different sizes were provided to view changes in forecast accuracy. Each data set contained 2 data sets of primary care level and secondary care level with 4 times the change of the number of attributes from 25 to 58 and the number of records from 300 to 11,000. Data were developed to predict the level of diabetes confirmed by specialist doctors. The experimental results showed that on average, the J48 algorithm showed the best model (99%) followed by Logistics (98%), RandomTree (95%), NaiveBayes Updateable (93%), BayesNet (84%) and AdaBoostM1 (67%). Ratio analysis also showed that the accuracy of the forecast model has increased up to 49%. The MAPKB model for the care of diabetes is designed with data change criteria dynamically and is able to develop the latest dynamic prediction models effectively.

keyword

IDM, Diabetes, decision making, simulation, accuracy.

Area

Knowledge Technology