Discovering sequential disease patterns in medical databases using freespan mining and prefikspan mining approach
| Main Authors: | Rostianingsih, Silvia, Satiabudhi, Gregorius, T, LEONITA KUMALASARI |
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| Format: | Article PeerReviewed application/pdf |
| Terbitan: |
, 2015
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| Subjects: | |
| Online Access: |
https://repository.petra.ac.id/16947/1/Publikasi1_01043_1843.pdf https://repository.petra.ac.id/16947/7/3._Peer_Review.pdf https://repository.petra.ac.id/16947/10/3._Discovering_Sequential_Disease_Patterns_in_Medical_Databases_using_Freespan_Mining_and_Prefikspan_Mining_Approach.pdf http://www.arpnjournals.com/jeas/volume_12_2014.htm https://repository.petra.ac.id/16947/ |
Daftar Isi:
- Dr. Soetomo General Hospital had computerized their system to stored inpatient#65533s history. With lots of data to be analysis, one of the needs is a decision support system in order to anticipate the spread of the disease. Therefore the hospital need a system to provide the sequential pattern of disease. One of the sequential pattern mining algorithm is pattern growth based approach. The result is sequential pattern of disease from particular area in a time period based on inpatient#65533s history. Input from user are time period, minimum support, province, and multi-dimensional. The system built with Java Net Beans 6.7 and Oracle 10g. This research showed that FreeSpan and PrefikSpan produce the same output. However, FreeSpan is more appropriate for dr. Soetomo General Hospital because the proccesing time is faster.