Cognitive Artificial Intelligence Decision Support System based on Knowledge Growing System - A D W Sumari, C O Sereati, I N Syamsiana, M N Wibisono, R Abdulharis, D R H Putra (International Journal of Advanced Science and Technology, 2020)
Accurate and faster decision making has been a challenge for systems based on Artificial Intelligence (AI). Since its emergence in the middle of 20th century, there has been a fast development in AI methods for decision making. The most often approaches used for decision making are from machine learning families such as support vector method (SVM), artificial neural network (ANN), k-nearest neighbor (kNN), Decision Tree, and naive Bayes (NB), which are based on supervised learning mechanism. The main problem in supervised learning is it needs many data or training set to be learnt during training phase. If the number of training set is not sufficient, the accuracy will be low, and this will be dangerous to be used as the basis for making decisions. In this paper, we propose a new approach for accurate and faster decision making by using a technology from Cognitive Artificial Intelligence (CAI) domain called as Knowledge Growing System (KGS). It has advantage to process multi-feature multi-label data which can be able to deliver more accurate and faster result. We also give an example of its usage in healthcare application, especially for medical diagnosis.