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--- abstract: 'Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs.' altloc: - http://www.ojhas.org/issue19/2006-3-1.htm chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - Kavitha S - Sarbadhikari SN - Rao AN creators_name: - family: S given: Kavitha honourific: '' lineage: '' - family: S given: Sarbadhikari honourific: '' lineage: N - family: N given: Ananth honourific: '' lineage: Rao date: 2006-12 date_type: published datestamp: 2006-12-22 department: ~ dir: disk0/00/00/53/20 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: - Kakkilaya BS editors_name: - family: Kakkilaya given: Srinivas honourific: Dr. lineage: Bevinje eprint_status: archive eprintid: 5320 fileinfo: /style/images/fileicons/application_pdf.png;/5320/1/2006%2D3%2D1.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'Decision support techniques, Metabolic diseases, Computer-assisted diagnosis, Expert system' lastmod: 2011-03-11 08:56:44 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: 3 pagerange: ~ pubdom: TRUE publication: Online Journal Of Health And Allied Sciences publisher: Dr. B.S. Kakkilaya refereed: TRUE referencetext: |- 1. Fernandez J, Saudubray J-M, Van Bergeh G. Eds, Inborn Metabolic Diseases: Diagnosis and Treatment. 4th ed, New York, Springer-Verlag, 2006 2. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Chapman & Hall / CRC Press, 1984 3. Hofestadt R, Mischke U, Scholz U. Knowledge Acquisition, Management and Representation for the Diagnostic Support in Human Inborn Errors of Metabolism, Stud Health Technol Inform. 2000; 77:857-62 4. Kauert R, Topel T, Scholz U, Hofestadt R. Information System for the Support of Research, Diagnosis and Therapy of Inborn Metabolic Diseases, Medinfo. 2001; 10(Pt 1): 353-6 5. Pince H, Cobbaert C, van de Woestijne M, Lissens W, Willems JL. Computer Aided Phenotyping of Dyslipoproteinemia, Int J Biomed Comput. 1988 Dec; 23(3-4):251-63 6. Wyett CE. “MetaNet” http://medexpert.imc.akh-wien.ac.at/metanet_info.html (Accessed July 2006). 7. Smith S, The Classification Algorithm http://www.cs.mdx.ac.uk/staffpages/serengul/The.Classification.algorithm.htm (Accessed July 2006). 8. Troendle JF, Liu A, Wu C, Yu KF. Sequential testing for efficacy in clinical trials with non-transient effects. Stat Med. 2005 Nov 15; 24(21): 3239-50 9. Hermann W, Wagner A, Kuhn HJ, Grahmann F, Villmann T. Classification of fine-motoric disturbances in Wilson's disease using artificial neural networks. Acta Neurol Scand. 2005 Jun;111(6):400-6 10. Baumgartner C, Bohm C, Baumgartner D, Marini G, Weinberger K, Olgemoller B, Liebl B, Roscher AA. Supervised machine learning techniques for the classification of metabolic disorders in newborns. Bioinformatics 2004 Nov 22; 20(17):2985-96. Epub 2004 Jun 4 relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:09:04 subjects: - OJHAS succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study' type: journale userid: 4338 volume: 5