Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

Abstract

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4–7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (n = 10,731), hypertension (n = 10,991), T2DM (n = 3889) and overweight/obesity (n = 10,980) were set as dependent variables. Nutritional intakes were set as independent variables. A DNN model comprising one input layer with 7 nodes, three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer and one output layer with one node were implemented in Python programming language using Keras with tensorflow backend. In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer. Structural equation modelling (SEM) was also performed to simultaneously estimate multivariate causal association between nutritional intake and overweight/obesity, dyslipidemia, hypertension and T2DM. The DNN model showed the higher prediction accuracy with 0.58654 for dyslipidemia, 0.79958 for hypertension, 0.80896 for T2DM and 0.62496 for overweight/obesity compared with two other machine leaning models with five-folds cross-validation. Prediction accuracy for dyslipidemia, hypertension, T2DM and overweight/obesity were 0.58448, 0.79929, 0.80818 and 0.62486, respectively, when analyzed by a logistic regression, also were 0.52148, 0.66773, 0.71587 and 0.54026, respectively, when analyzed by a decision tree. This study observed a DNN model with three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer had better prediction accuracy than two conventional machine learning models of a logistic regression and decision tree.

Association between Health Literacy and Prevalence of Obesity, Arterial Hypertension, and Diabetes Mellitus

“…Arterial hypertension is the leading metabolic risk factor for premature death at the global level, accounting for 19% of deaths worldwide [ 3 ], accompanied by overweight/obesity and elevated blood glucose [ 4 , 5 ]. Metabolic syndrome refers to a group of pathological conditions that includes central obesity, arterial hypertension, and carbohydrate and lipid metabolism disorders, which increases the risk of developing type 2 diabetes, cardiovascular diseases, and obesity energy intake [ 6 ]. Obesity cannot be viewed as a simple phenomenon; instead, it is a complex concept of cumulative imbalance of energy intake and consumption in the context of complex interactions of environmental factors that influence behavior by modulating energy intake and consumption [ 7 ] with significant influence of genetic and developmental factors.…”

Structural modelling for assessing the effectiveness of system for countering legalization of illicit money

“…According to this method, various software is used in calculations: Lavaan (R Package) (Yeves Rosseel, 2012), Python (Kim H. et al, 2021), STATISTiCA software (Kerimkulova D. et al, 2021) etc.…”

Sule Lamido University Journal of Science and Technology (SLUJST) Vol. 3 No. 1&2 [June, 2022], pp. 113-121113Obesity Level ClassificationBased on Decision Tree and Naïve Bayes Classifiers

“…The obesity rate is increasing gradually; from prior research, obesity is a serious health disease around the globe. Some of the major complications caused by obesity include stroke, heart disease, diabetes, hypertension, dyslipidemia, and metabolic syndrome (Kim, Lim, & Kim, 2021). Many attributes such as height, level of sleep, calories intake, etc.…”

“…The prediction of the level of obesity is very critical as early intervention programs and strategies that target the risks associated with each level of obesity can be developed. Moreover, the possible influencers of each level of obesity can also be identified (Kim et al, 2021).…”

“…Although the usage of machine learning and other data mining techniques for classification has increased in the healthcare domain (obesity, covid-19, cancer e.t.c), it still has many challenges and limitations, especially in obesity prediction or classification (Kim et al, 2021). Several challenges are faced in the obesity level.…”