An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital
Abstract: Diabetes is a
chronic disease and major problem of morbidity and mortality in developing
countries. The International Diabetes Federation estimates that 285 million
people around the world have diabetes. This total is expected to rise to 438
million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common
type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM
from various factors or symptoms became an issue which was not free from false
presumptions accompanied by unpredictable effects. According to this context,
data mining and machine learning could be used as an alternative way help us in
knowledge discovery from data. We applied several learning methods, such as
instance based learners, naive bayes, decision tree, support vector machines,
and boosted algorithm acquire information from historical data of patient’s
medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules
are extracted from Decision tree to offer decision-making support through early
detection of T2DM for clinicians.
Author: Bayu Adhi Tama,
Rodiyatul F. S. Rodiyatul F. S., Hermansyah
Journal Code: jptkomputergg110036
