Normal view MARC view ISBD view

Diabetes detection system / Nagor Nisah Raja Mohammad

By:

Nagor Nisah Raja Mohammad

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2012Description: vii, 109 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0002298(Local)

Subject(s):

Diabetes -- Diagnosis

Dissertation note: Project paper (Bachelor of Computer Science (Networking)) -- Universiti Malaysia Pahang – 2012 Abstract: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	RA645.D5 N34 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000079007
Final Year Report	UMPLIB GAMBANG	CD 7681 \| RA645.D5 N34 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000079008

Project paper (Bachelor of Computer Science (Networking)) -- Universiti Malaysia Pahang – 2012

Bibliography : p. 63-65

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.