The Decision Support System (DSS) Application to Determination of Diabetes Mellitus Patient Menu Using a Genetic Algorithm Method

Zuliyana, Nia; Suseno, Jatmiko Endro; Adi, Kusworo

doi:10.1051/e3sconf/20183110006

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 1: Chromosome Variables The variables of chromosomes are shown in Table 4. [3] 𝑅 [4] > 𝑃𝑐[4] 𝑅 [5] < 𝑃𝑐 [5] → 𝑅 [5] 𝑅 [6] > 𝑃𝑐 [6] …”

Section: Genetic Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Optimization using Genetic Algorithm in Food Composition

Adriyendi¹,

Melia²

2021

IJCDS

View full text Add to dashboard Cite

This paper aims to process daily food consumption based on the Guideline to Healthy Food or the principle of Healthy 4 Perfect 5 (H4P5). The Guideline to Healthy Food does not include the portion and size of daily food consumption, therefore it is perfected into the Guidelines to Balanced Nutrition (GBN). GBN is performed by consuming three servings of protein vegetables, three servings of animal protein, eight servings of carbohydrates, five servings of vegetables, five servings of fruits, and eight servings of water. Each part of the portion has a proportional size and dose based on GBN. The portions are measured by food composition using the optimization process using a Genetic Algorithm (GA). Optimization by a using Genetic Algorithm yielded three outputs, namely, first, the initial objective function (0) compared to the generative objective function (1) produces a value of 6.13: 4.72 (there is an increase in chromosomes, because, the smaller the value, the better the chromosome is). Second, probability mutation is 50% of mutation rate, crossover probability is 5%, the maximum generation is 36, the maximum fitness point is 0.240806, and the size of the population is 6. Third, optimization using the Genetic Algorithm in food composition produces one food model, one consumption model, and one composition model. Thus, the result of this research (food model, consumption model, and composition model) can be a recommendation for food consumption patterns (normal condition) and a reference for national food consumption policies.

show abstract

“…Step 1: Chromosome Variables The variables of chromosomes are shown in Table 4. [3] 𝑅 [4] > 𝑃𝑐[4] 𝑅 [5] < 𝑃𝑐 [5] → 𝑅 [5] 𝑅 [6] > 𝑃𝑐 [6] …”

Section: Genetic Algorithmmentioning

confidence: 99%

“…RELATED WORK One popular computational algorithm for optimization problems is the Genetic Algorithm [4]. A Genetic Algorithm has been proposed by many researchers on food problems.…”

mentioning

confidence: 99%

Optimization using Genetic Algorithm in Food Composition

Adriyendi¹,

Melia²

2021

IJCDS

View full text Add to dashboard Cite

show abstract

“…Dalam jurnal milik Zuliyana et al [8], menjelaskan bahwa "Genetic algorithm are selection evolution operations that work probabilistically against several possible solutions to make genetic algorithm very effective in global optimization".…”

Section: Landasan Teorimentioning

confidence: 99%

Penentuan Komposisi Bahan Makanan Bagi Penderita Diabetes Melitus Menggunakan Algoritma Genetika

Efendi

Herawati

2021

proxies

View full text Add to dashboard Cite

Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. Insulin is a hormone that regulates blood sugar. The composition of food ingredients is very important for diabetics. In determining optimal food combinations especially for diabetics are still very difficult if done manually. To solve this problem, this project used genetic algorithms. With existing food data, food combinations can be made to suit the needs of each patient. Making this project using java programming. The test is done twice, where each test is done with 2 different choices. Optimal results are obtained from Elitism Selection and also a faster time.

show abstract

A Novel Approach for Determining Meal Plan for Gestational Diabetes Mellitus Using Artificial Intelligence

Huynh

Hoàng

2020

The Computer Journal

View full text Add to dashboard Cite

Estimating energy expenditure and meal plan plays important roles in the treatment of gestational diabetes mellitus, which is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Some approaches have been proposed; however, they have limitations including high cost, relative complexity, trained personnel requirements or locality. In this study, we propose an approach for estimating the energy expenditure and meal plan by using artificial intelligence. The proposed approach consists of three main stages including energy expenditure estimation, macronutrient intake estimation and meal plan determination. The neural network is used to estimate the energy expenditure, and then the meal plan is determined by using the genetic algorithm (GA), which is a popular method for solving optimization problems based on natural selection and genetics. The fitness function with penalty was used in GA to deal with constraint problems. The proposed method can obtain the root mean square error and mean absolute percentage error of 15.23 ± 7.4 kcal and 1 ± 0.8%, respectively. The Pearson correlation coefficient, which measures the strength of the association between the two measurements, was 0.99. In meal plan determination, the results from GA agreed with those from nutritionists. The Pearson correlation coefficient was 0.95. The energy expenditure and meal plan are determined by soft computing with flexible ways. They can adapt to particular regions or group of patients.

show abstract

The Decision Support System (DSS) Application to Determination of Diabetes Mellitus Patient Menu Using a Genetic Algorithm Method

Cited by 3 publications

References 5 publications

Optimization using Genetic Algorithm in Food Composition

Optimization using Genetic Algorithm in Food Composition

Penentuan Komposisi Bahan Makanan Bagi Penderita Diabetes Melitus Menggunakan Algoritma Genetika

A Novel Approach for Determining Meal Plan for Gestational Diabetes Mellitus Using Artificial Intelligence

Contact Info

Product

Resources

About