  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, 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.
