Nutr Res Pract.  2023 Dec;17(6):1255-1266. 10.4162/nrp.2023.17.6.1255.

Classification and prediction of the effects of nutritional intake on diabetes mellitus using artificial neural network sensitivity analysis: 7th Korea National Health and Nutrition Examination Survey

• Chang K
• Yoo S
• Lee S

• KMID: 2548890
• DOI: http://doi.org/10.4162/nrp.2023.17.6.1255

Abstract

BACKGROUND/OBJECTIVES
This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults.
SUBJECTS/METHODS
Participants aged over 65 years from the 7th (2016–2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB ® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output.
RESULTS
Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat.
CONCLUSIONS
In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

Keyword

Diabetes mellitus; artificial intelligence; sensitivity; aged

Figure

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  Fig. 1 Flow chart for selecting the study population.KNHANES, Korea National Health and Nutrition Examination Survey.

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  Fig. 2 Neural network schematic diagram.

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  Fig. 3 Artificial neural network and sensitivity analysis process.

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  Fig. 4 Minimum square error according to number of epochs.

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  Fig. 5 Predicted vs. observed values in the (A) training and (B) test sets.

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  Fig. 6 Sensitivity index values for 11 inputs.

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