Predictive Classification of ECG Parameters Using Association Rule Mining
Data mining is the procedure of extricating valuable information from the tremendous information stored in the database. Association rule mining is one of the most important and powerful data mining techniques. Association rule mining is normally carried
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Abstract Data mining is the procedure of extricating valuable information from the tremendous information stored in the database. Association rule mining is one of the most important and powerful data mining techniques. Association rule mining is normally carried out in two stages: first is to find frequent item set and second is to utilize those item sets to recognize the association rules. In recent medical history of cardiac arrests it has been observed that a huge gap exists in interpreting ECG data among differently skilled doctors. In this paper we will use the principle of meta-analysis and will reduce the gap between the interpretations of different doctors by employing statistical techniques like correlation and multiple linear regression. We would also generate rules using predictive apriori association rule mining among the various attributes of ECG to classify whether a patient requires an ECG before a cardiac arrest or not. The purpose of carrying out this work is to reduce the fatality rate and be able to predict that whether a patient requires ECG before actually facing a cardiac arrest and to minimize the cases of wrong interpretation. Keywords Data mining
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Association rule mining
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Predictive apriori
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ECG data
1 Introduction Data mining is the procedure of fascinating knowledge or patterns from extensive databases. There are various techniques that have been utilized to find such sort of information, majority of them results from the machine learning and statistical area [1].
K. Tyagi (✉) ⋅ S. Thakur Department of Computer Science & Engineering, ASET Amity University, Noida, Uttar Pradesh, India e-mail: [email protected] S. Thakur e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.K. Bhatia et al. (eds.), Advances in Computer and Computational Sciences, Advances in Intelligent Systems and Computing 554, https://doi.org/10.1007/978-981-10-3773-3_60
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Association rule mining is the powerful data mining techniques which are normally carried out in two steps: first is to find frequent item set and second is to utilize those item sets to recognize the association rules. Keeping in mind the end goal to produce relationship among different work factors, association rule mining can help us to create association rules utilizing the idea of CAR. The extent of this paper is to generate the association rules using predictive apriori algorithm that will produce best rules based on increasing support–confidence threshold over a dataset generated by doctor responses. The paper is divided into five sections. The second section following this introduction is a detailed literature discussing the factors and the predictive apriori algorithm. The third section is a simple experimental setup followed by results in the fourth section. The final section will be a detailed discussion on the results along with plausible conclusion.
2 Literature Survey In this section we will discuss about the essentials of association rule mining and basic terminologies relate
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