RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Abstract

Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient's medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA-SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises ''similar'' concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

“…The current algorithms specific to frequent itemset mining are largely divided into two major types: exact algorithms and heuristic algorithms. The most classical exact algorithms are the Apriori algorithm [10] and FP-Growth algorithm [11], as well as many improved algorithms derived from the two algorithms [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Semantic data mining in the information age: A systematic review

“…Kamkhad et al 117 proposed a method that collaborates particle swarm optimization (PSO) with semantic data mining for the data preparation process, an essential first process for providing ‘clean’ data as input to ontology construction and the semantic data‐mining process. Sornalakshmi et al 118 presented an approach that combines sequential minimal optimization (SMO) with the Apriori algorithm, for generating efficiency rules and predicting the proper physiological parameters, in the healthcare domain in this case. The proposed approach improved the accuracy of the analysis and reduced the execution time.…”

A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)

“…CBA is one of the AC developed algorithms, as well as it uses the function of generating Apriori candidates to discover new rules of the association from datasets. An improved Apriori algorithm (EAA) for a sequential minimal optimization (SMO) is proposed based on the knowledge of context ontology (EAA-SMO) [29] showed an increase in the accuracy of the whole process. Recently, the Intelligent Apriori (lAP) algorithm [30] uses an extension of the Apriori algorithm for the mining and processing of the data obtained using patterns and relationships, using the frequent itemsets.…”