Data Mining and Meta-Analysis of Psoriasis Based on Association Rules

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.

Effect of Traditional Chinese Medicine on Allergic Rhinitis in Children under Data Mining

The data mining analysis of the medication rule and the curative effect of traditional Chinese medicine in treating allergic rhinitis in children was performed by using the association rule Apriori algorithm. The model of interest degree was introduced to improve the Apriori algorithm, and the performance difference of the algorithm before and after improvement was analyzed. Traditional Chinese medicine prescriptions for the treatment of allergic rhinitis in children were selected from the dictionary of Chinese medicine formulations. The frequency, frequent itemsets, and the improved Apriori algorithm of each prescription were analyzed comprehensively. The results showed that both the execution time of the improved Apriori algorithm and the number of mining association rules were signally lower. 102 Chinese herbal compounds were selected, in which the occurrence frequency of Flos magnoliae was the highest (67 times, 5.33%). The occurrence frequency of diaphoretic drugs was the highest (412 times, 32.78%) in drug types. The occurrence frequency of Yu Ping Feng powder was the highest (21 times, 20.59%) in the Chinese herbal compound. After the association rule analysis of the improved Apriori algorithm, Perilla frutescens, Saposhnikovia divaricata, ginseng, Notopterygium root, and Astragalus propinquus Schischkin were often mixed with liquorice, and Flos magnoliae were usually mixed with Fructus xanthii and black plum. Compared with the conditions before treatment, the sign scores of children with allergic rhinitis were remarkably decreased after treatment with traditional Chinese medicine compounds (P < 0.05). The mining performance of the Apriori algorithm was improved by introducing an interest-based model. The treatment of traditional Chinese medicine on allergic rhinitis in children was combined with children's physiological and pathological characteristics of children, which used mild medicines.