Detecting impending clinical syndromes of Hepatocellular carcinoma by implementing Hierarchical feature of selection process based on improved optimization algorithm
Hepatocellular carcinoma or HCC had been categorized as a malignant tumor. The symptoms of HCC is hidden and cold not receive primary therapeutic aids. In compared to other medicine center, traditional Chinese medicine (TCM) that could detect HCC symptoms and cure it. This study had presented a new improved artificial bee colony (ABC) method in order to find existing syndromes of HCC. At first stage, we had formed the hierarchical feature demonstration using three layer tree models. The symptoms and positive number of an ill are leaf nodes and root of the tree, whereas the syndrome feature of internal layer is pulled out from a range of appeared symptoms. At the second stage, in the newly decreased feature space, we had used an enhanced model on the basis of proposed algorithm in order to investigate on the optimal syndromes. According to the obtained outcomes of feature selection, we had extracted out the usual relations of symptoms and syndromes from Bayesian system. In this conducted study, we had compound various symptoms. By applying the suggested method we had recognized many syndromes that ameliorate detecting preciseness. Ultimately, in order to show the common connection of symptoms and syndrome level we had used Bayesian method. The outcomes demonstrated that our suggested calculation model has the ability to intense the detection process of HCC.