Application of Apriori Improvement Algorithm in Asthma Case Data Mining.

Yi Zheng,Dengjun Wei,Peipei Chen,Meifang Wang,Biyu Chen,Rahim Khan

doi:10.1155/2021/9018408

Yi Zheng, Dengjun Wei + Show 4 more

Open Access

https://doi.org/10.1155/2021/9018408

Copy DOI

Abstract

In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.

Highlights

Due to the rapid development in the Internet of Medical ings (IoMT), wearable devices such as sensor and actuator, the smart healthcare systems continue to expand and generate a large amount of data every day
To verify the exceptional performance of the proposed enhanced Apriori algorithm, it and existing schemes are implemented in java. ese algorithms were tested on large and benchmark data set under the same conditions, i.e., data set size, computational power, and resources
The individual data width is 39; we have decided to experiment with medical case data and analyze the results using the proposed enhanced Apriori algorithm based on bit-wise operations and preparing. e results presented in Table 2 are some of the sets of transactions that lead to sleeping apnea hypoventilation syndrome and their support

Summary

Introduction

Due to the rapid development in the Internet of Medical ings (IoMT), wearable devices such as sensor and actuator, the smart healthcare systems continue to expand and generate a large amount of data every day. We have concluded that the traditional Apriori algorithm generates all frequent item sets that satisfy the minimum support threshold values in a given application domain. One of the challenging issues associated with the traditional Apriori algorithm is that it has to scan the database multiple times and generate a large set of candidate items. To resolve this issue, an enhance version of the traditional Apriori algorithm, which is based on the bit-string logic algorithm and called the AprioriBSO algorithm, is presented. Each item is coded to generate a coded bit string: the length of the code is the number of items in the library, and if a single start start input minsup; k=1

Join step to generate candidate

Cardiogenic Variant Mixed other

Number of items

Conclusion and Future Work