Chronic kidney failure data management system with automatic classification

• Main Author: Murugiah, Khovarthen
• Format: Thesis
• Language: English
• Published: 2015
• Subjects: R Medicine (General)
• Online Access: http://eprints.utm.my/id/eprint/54651/1/KhovarthenMurugiahMFBME2015.pdf; http://eprints.utm.my/id/eprint/54651/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86529

Chronic kidney failure (CKF) is an irreversible loss of renal function for at least three months. The number of population with CKF and end-stage renal disease (ESRD) is increasing worldwide, places an enormous human, economic and social burden on the healthcare system. Targeted screening and early intervention are necessary to reduce the burden of the disease. Currently, most of the government hospitals and clinics are still using paper based record for CKF stage classification data management. This current system may cause severe problems such as difficulties to understand handwriting (laboratory test), longer data transfer time from laboratory to clinician office and paper based estimate Glomerular Filtration Rate (eGFR) calculation to determine CKF stage which may lead to many medical error or misdiagnosis. This project develops a user friendly electronic data management system which can store electronic health record and able to perform automatic eGFR value calculation based on Modification Diet of Renal Disease (MDRD) equation which authorized by MoH Malaysia for CKF stage classification. This system will assists health professional to store patient’s information such as personal details, physical appearance, medical history, laboratory test results efficiently and assist clinician to classify the stage of CKF of patient by automatic calculation of eGFR value. This system is developed using MySQL and Microsoft Visual Studio C#. Based on comparison with other related system, this proposed system offer better features in term of data management and data storage of patient details, laboratory test results and clinician decision details electronically. At the same time, it can compute semi-automated classification of CKF stage 1,2,3,4 and 5 with the help of clinician.