Abstract:
Diabetes mellitus is a common disease of human
body caused by a group of metabolic disorders where the sugar
levels over a prolonged period is very high. It affects different
organs of the human body which thus harm a large number of
the body's system, in particular the blood veins and nerves.
Early prediction in such disease can be controlled and save
human life. To achieve the goal, this research work mainly
explores various risk factors related to this disease using
machine learning techniques. Machine learning techniques
provide efficient result to extract knowledge by constructing
predicting models from diagnostic medical datasets collected
from the diabetic patients. Extracting knowledge from such
data can be useful to predict diabetic patients. In this work, we
employ four popular machine learning algorithms, namely
Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest
Neighbor (KNN) and C4.5 Decision Tree (DT), on adult
population data to predict diabetic mellitus. Our experimental
results show that C4.5 decision tree achieved higher accuracy
compared to other machine learning techniques.
