Abstract:
Native Americans living in Arizona called PIMA have several medical
problems such as diabetes. Diabetes prediction of females in this population
has been done using various machine learning techniques. The objective of
this study is to compare eight supervised machine learning models to identify
the best algorithm with a low bias-variance trade-off for the diagnosis of type
II diabetes among female PIMA population. Eight prediction models namely
logistic regression, decision tree, random forest, naïve bayes, k-nearest
neighbor, support vector machine, gradient boosting, and artificial neural
network (ANN) were developed for type II diabetes using the data published
by the National Institute of Diabetes, Digestive and Kidney Diseases in the
USA (PIMA Indian Diabetes Dataset). Among the 768 patient records, 430
(50% with diabetes and 50% without diabetes) were used to train the models
to reduce data biasness, and the remaining 338 records were used for testing.
The performance of each model was evaluated and compared using testing
accuracy, mean squared error (MSE), sensitivity, precision, and F1-score. The
results showed that the random forest model has the highest testing accuracy
of 83.12% and the lowest MSE. This result shows that most significant
predictor variables are number of pregnancies, insulin level, BMI level, and
age. The ANN model achieved the highest MSE, due to the limited number of
training data. Therefore, the random forest model with number of 50 subtrees
is the most accurate machine learning model that can be used to diagnose type
II diabetes in the PIMA Indian Diabetes Dataset.
