Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients

Abstract

The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networks method–based data-analysis module, which is used to specify the severity of coronavirus symptoms and classify cases as mild, moderate, and severe, and (2) autonomous decision-making module–based association rules mining method. This method allows the autonomous generation of the adequate decision based on the FP-growth algorithm and the distance between objects. To build the Bayesian Network model, we propose a novel data–based method that enables to effectively learn the network’s structure, namely, MIGT-SL algorithm. The experimentations are performed over pre-processed discrete dataset. The proposed algorithm allows to correctly generate 74%, 87.5%, and 100% of the original structure of ALARM, ASIA, and CANCER networks. The proposed Bayesian model performs well in terms of accuracy with 96.15% and 94.77%, respectively, for binary and multi-class classification. The developed decision-making model is evaluated according to its utility in solving the decisional problem, and its accuracy of proposing the adequate decision is about 97.80%. Graphical abstract

Modeling OTP Delivery Notification Status through a Causality Bayesian Network

“…For each method's accuracy and confidence values, the a priori algorithm employs confidence values, and the Bayesian network method uses accuracy values to compare these two approaches [20]. Table 1 illustrates that, on average, the Bayesian network's dependability value is higher than the a priori algorithm method's.…”