Diabetes detection system / (Record no. 4424)

MARC details
000 -LEADER
fixed length control field 01631nam a2200241 a 4500
001 - CONTROL NUMBER
control field vtls000076556
003 - CONTROL NUMBER IDENTIFIER
control field KUKTEM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251114204557.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140107t2012 my da f m 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0002298(Local)
039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE]
Level of rules in bibliographic description 201905271025
Level of effort used to assign nonsubject heading access points shamsul
-- 201401071027
-- nabilah
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) RA645.D5 N34 2012 rs Bc.
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Nagor Nisah Raja Mohammad
245 10 - TITLE STATEMENT
Title Diabetes detection system /
Statement of responsibility, etc. Nagor Nisah Raja Mohammad
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Kuantan, Pahang :
Name of publisher, distributor, etc. UMP,
Date of publication, distribution, etc. 2012
300 ## - PHYSICAL DESCRIPTION
Extent vii, 109 p. :
Other physical details ill. ;
Dimensions 30 cm. +
Accompanying material 1 CD-ROM
502 ## - DISSERTATION NOTE
Dissertation note Project paper (Bachelor of Computer Science (Networking)) -- Universiti Malaysia Pahang – 2012
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Bibliography : p. 63-65
520 3# - SUMMARY, ETC.
Summary, etc. This thesis proposes the development of Diabetes Detection System (DDS) capable of detecting potential diabetes based on the rule-based technique. Specifically, DDS enables the user to select the symptoms that they have without having to see the doctor as part of early screening. Using these symptoms, DDS determines whether or not the user is potentially at risk for diabetes. In the current version, DDS is capable to detect three possible outcomes: Healthy, Diabetic Type 1, and Diabetic Type 2. Implemented using Adobe Dreamweaver CS6 and XAMPP, DDS adopts forward-chaining rules with live input data against the conditions (IF parts) of the rules. DDS represents our research vehicle to investigate the applicability of rule-based technique for symptomatic diseases.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Diabetes
General subdivision Diagnosis
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost   Not for loan UMPLIB GAMBANG UMPLIB GAMBANG 04/09/2019   RA645.D5 N34 2012 rs Bc. 0000079007 04/09/2019 1 04/09/2019 Final Year Report
  Not lost   Not for loan UMPLIB GAMBANG UMPLIB GAMBANG 04/09/2019   CD 7681 | RA645.D5 N34 2012 rs Bc. 0000079008 04/09/2019 1 04/09/2019 Final Year Report

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