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Original ArticleOpen Access
A Simple Prediction Rule and a Neural Network Model to Predict Pancreatic Beta-Cell Reserve in Young Adults with Diabetes Mellitus
Thamprajamchit S 
,
Ongphiphadhanakul B ,
Krittiyawong S ,
Chanprasertyothin S ,
Bunnag P ,
Rajatanavin R ,
Puavilai G
,
Ongphiphadhanakul B ,
Krittiyawong S ,
Chanprasertyothin S ,
Bunnag P ,
Rajatanavin R ,
Puavilai G In the present study we developed and assessed the performance of a simple prediction
rule and a neural network model to predict beta-cell reserve in young adults with diabetes.
Eighty three young adults with diabetes were included in the study. All were less than 40 years
old and without apparent secondary causes of diabetes. The subjects were randomly allocated to
2 groups; group 1 (n
=
59) for developing a prediction rule and training a neural network, group
2 (n
=
24) for validation purpose. The prediction rule was developed by using stepwise logistic
regression. Using stepwise logistic regression and modification of the derived equation, the
patient would be insulin deficient if 3(waist circumference in em)
+
4(age at diagnosis) < 340
in the absence of previous diabetic ketoacidosis (DKA) or
<
400 in the presence of previous
DKA. When tested in the validation set, the prediction rule had positive and negative predictive
values of 86.7 per cent and 77.8 per cent respectively with 83.3 per cent accuracy while the
ANN model had a positive predictive value of 88.2 per cent and a negative predictive value of
100 per cent with 91.7 per cent accuracy. When testing the performance of the prediction rule
and the ANN model compared to the assessment of 23 internists in a subgroup of 9 diabetics
whose age at onset was less than 30 years and without a history of DKA, the ANN had the
highest ability to predict beta-cell reserve (accuracy
=
88.9), followed by the prediction rule
(accuracy
=
77.8%) and assessments by internists (accuracy
=
60.9%).
We concluded that beta-cell reserve in young adults with diabetes mellitus could be
predicted by a simple prediction rule or a neural network model. The prediction rule and the
neural network model can be helpful clinically in patients with mixed clinical features of type 1
and type 2 diabetes.
Key word : Diabetes Mellitus, Beta-Cell Reserve, Beta-Cell Function, Prediction Rule, Neural
Network
rule and a neural network model to predict beta-cell reserve in young adults with diabetes.
Eighty three young adults with diabetes were included in the study. All were less than 40 years
old and without apparent secondary causes of diabetes. The subjects were randomly allocated to
2 groups; group 1 (n
=
59) for developing a prediction rule and training a neural network, group
2 (n
=
24) for validation purpose. The prediction rule was developed by using stepwise logistic
regression. Using stepwise logistic regression and modification of the derived equation, the
patient would be insulin deficient if 3(waist circumference in em)
+
4(age at diagnosis) < 340
in the absence of previous diabetic ketoacidosis (DKA) or
<
400 in the presence of previous
DKA. When tested in the validation set, the prediction rule had positive and negative predictive
values of 86.7 per cent and 77.8 per cent respectively with 83.3 per cent accuracy while the
ANN model had a positive predictive value of 88.2 per cent and a negative predictive value of
100 per cent with 91.7 per cent accuracy. When testing the performance of the prediction rule
and the ANN model compared to the assessment of 23 internists in a subgroup of 9 diabetics
whose age at onset was less than 30 years and without a history of DKA, the ANN had the
highest ability to predict beta-cell reserve (accuracy
=
88.9), followed by the prediction rule
(accuracy
=
77.8%) and assessments by internists (accuracy
=
60.9%).
We concluded that beta-cell reserve in young adults with diabetes mellitus could be
predicted by a simple prediction rule or a neural network model. The prediction rule and the
neural network model can be helpful clinically in patients with mixed clinical features of type 1
and type 2 diabetes.
Key word : Diabetes Mellitus, Beta-Cell Reserve, Beta-Cell Function, Prediction Rule, Neural
Network
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