Mining Health Care Sequences using Weighted Associative Classifier

Mining Health Care Sequences using Weighted Associative Classifier

Sunita Soni

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Dr. O.P. Vyas

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α Chhattisgarh Swami Vivekanand Technical University

Mining Health Care Sequences using Weighted Associative Classifier

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Abstract

This paper proposes the general framework for mining sequences from health care database. The database is a relational model consisting of set of temporal records of individual patient consisting of basic information of the patient ie Patient_ID, age, gender etc. the second part is a series of sequences representing the set of treatment given to the patient during regular visit to the doctor and the third part is class label. Similarity search of sequences is performed to convert the database of sequences, to the database of items, so that apriori algorithm can be applied. Weighted association rule mining has been performed to find the frequent sequence of treatment provided to the patient. Classification association rules (CAR) having positive class label as consequent, represents the frequent sequence of treatment given to the patient for successful treatment. With the experimental results, author feels confident in declaring that the framework is feasible in the medical domain.

References

18 Cites in Article

Reference Format

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S Concaro,L Sacchi,C Cerra,P Fratino,R Bellazzi (2008). Guidance FDA (2001) using a REML UN model. Then, this estimate is asymptotically normally distributed, unbiased with E[νˆ ] = δ +σ − (σ )− 0.04(c ) and has variance of Var[νˆ ] = 4σ δ + l + 2l − 2l + 2l To assess PBE we ‘plug-in’ estimates of δ and the variance components and calculate the upper bound of an asymptotic 90% conﬁdence interval. If this upper bound is below zero we declare that PBE has been shown. Using the code in Appendix B and the data in Section 7.4, we obtain the value −0.24 for log(AUC) and the value −0.19 for log(Cmax). As both of these are below zero, we can declare that T and R are PBE. 7.6 ABE for a replicate design Although ABE can be assessed using a 2× 2 design, it can also be as-sessed using a replicate design. If a replicate design is used the number of subjects can be reduced to up to half that required for a 2 × 2 de-sign. In addition it permits the estimation of σ and σ . The SAS code to assess ABE for a replicate design is given in Appendix B. Using the data from Section 7.4, the 90% conﬁdence interval for µ is (−0.1697,−0.0155) for log(AUC) and (−0.2474,−0.0505) for log(Cmax). Exponentiating the limits to obtain conﬁdence limits for exp(µ ), gives (0.8439,0.9846) for AUC and (0.7808,0.9508) for Cmax. Only the ﬁrst of these intervals is contained within the limits of 0.8 to 1.25, there-fore T cannot be considered average bioequivalent to R. To calculate the power for a replicate design with four periods and with a total of n subjects we can still use the SAS code given in Section 7.3, if we alter the formula for the variance of a diﬀerence of two obser-vations from the same subject. This will now be σ +σ instead of σ , where σ is the subject-by-formulation interaction. Note the use of σ rather than 2σ as used in the RT/TR design. This is a result of the estimator using the average of two measurements on each treatment on each subject. One advantage of using a replicate design is that the number of sub-jects needed can be much smaller than that needed for a 2×2 design. As an example, suppose that σ = 0, and we take σ = 0.355 and α = 0.05, as done in Section 7.3. Then a power of 90.5% can be achieved with only 30 subjects, which is about half the number (58) needed for the 2 × 2 design..

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Sunita Soni. 2014. \u201cMining Health Care Sequences using Weighted Associative Classifier\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C2): .

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GJCST Volume 14 Issue C2
Pg. 27- 35

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

prediction sequence mining weighted associative classifier weighted confidence weighted support

Submission ReceivedDecember 8, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

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May 15, 2014

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Sunita Soni, Dr. O.P. Vyas (PhD/Dr. count: 1)

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