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Comprehensive Association Rules Mining of Health Examination Data with an Extended FP-Growth Method

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Abstract

With the booming of social media and health informatics, there exists a pressing need for a powerful tool to sustain comprehensive analysis of public and personal health information. In particular, it should be able (1) to maximize the discovery of association rules amongst data items and (2) to handle the rapid growing data scale. The FP-Growth algorithm is a salient association rule learning method in exploring potential relation in database possibly with a lack of priori knowledge. It has the merits of low time & space complexity, whereas it cannot handle negative association rules which is necessary in comprehensive mining of health data. In order to enable comprehensive discovery of association rules, this study extends the FP-Growth algorithm to mine both positive and negative frequent patterns, namely the PNFP-Growth framework. The extended approach also adopts a prune strategy to filter out misleading patterns to the most by correlating the negative data items and the positive ones. Experiments had been performed to evaluate the performance of the PNFP-Growth over a public data set and a database consisting of thousands of people’s real health examination information (collected within 5 years from the date of this publication). The results indicate that (1) the PNFP-Growth can excavate more patterns than the traditional counterpart does while it is still highly efficient, and (2) the analysis upon the health examination data is informative and well complies with the clinical practices, e.g., more than 30 % people suffering from hypertension are having high systolic pressure and liver problems.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 61272314, 81402760), Fundamental Research Funds for the Central Universities (Nos. 2042015kf1009, 211410100028 (WHU), CCNU16A02020(CCNU)), Science & Technology Supporting Program in Hubei province (No. 2015BAA113), Humanities and Social Sciences Foundation of the Ministry of Education (No. 14YJAZH005), the Natural Science Foundation of Jiangsu Province (No. BK20161563).

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Authors and Affiliations

  1. Wuhan University, Wuhan, China

    Bowei Wang, Dan Chen, Jindong Zhang, Yifu Duan & Ruimin Hu

  2. Hangzhou Dianzi University, Hangzhou, China

    Benyun Shi

  3. Central China Normal University, Wuhan, China

    Jingying Chen

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  1. Bowei Wang
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  2. Dan Chen
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  3. Benyun Shi
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  4. Jindong Zhang
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  5. Yifu Duan
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  6. Jingying Chen
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  7. Ruimin Hu
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Correspondence to Dan Chen or Benyun Shi.

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Wang, B., Chen, D., Shi, B. et al. Comprehensive Association Rules Mining of Health Examination Data with an Extended FP-Growth Method. Mobile Netw Appl 22, 267–274 (2017). https://doi.org/10.1007/s11036-016-0793-6

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