Abstract
Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology.
Highlights
Association rules (ARs), a branch of data mining, have been studied successfully and extensively in many application domains including market basket analysis, intrusion detection, diagnosis decisions support, and telecommunications
The datasets have been collected from different medical blog sites: (i) Cancer Survivors Network [http://csn.cancer.org/], Inputs: min sup: minimum support; min conf: minimum confidence; frequent itemsets (FIS); infrequent itemset (inFIS) Output: positive association rules (PARs): set of [+ve]ARs; negative association rules (NARs): set of [−ve]ARs; (1) PAR = φ; NAR = φ; (2) /∗ generating all association rules from FIS. ∗/ For each itemset I in FIS do begin for each itemset A ∪ B = I, A ∩ B = φ do begin (2.1) /∗ generate rules of the form A ⇒ B. ∗/
There is a need to minimize the errors in diagnosis and maximize the possibility of early disease identification by developing a decision support system that takes advantage of the NARs
Summary
Association rules (ARs), a branch of data mining, have been studied successfully and extensively in many application domains including market basket analysis, intrusion detection, diagnosis decisions support, and telecommunications. Discovery of potential negative association rules is important to build a reliable decision support system. Patient-authored blogs have become an important component of modern-day healthcare These blogs can be effectively used for decision support and quality assurance. We have investigated the efficient mechanism of identifying positive and negative associations among medications, symptoms, and laboratory results using state-ofthe-art data mining technology. Rules of the form A ⇒ B or A ⇒ ¬B can help explain the presence or absence of different factors/variables Such types of associations can be useful for building decision support systems in the healthcare sector. The proposed model for extracting positive and negative association rules is presented in Section 5 and the final section of this paper gives the details of the experimental results, comparisons, and conclusions.
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