Classification of toddler nutritional status using fuzzy inference system (FIS)

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1 Classification of toddler nutritional status using fuzzy inference system (FIS) Dian Permatasari, Isnaini Nur Azizah, Hanifah Latifah Hadiat, and Agus Maman Abadi Citation: AIP Conference Proceedings 868, 47 (27); View online: View Table of Contents: Published by the American Institute of Physics Articles you may be interested in Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model AIP Conference Proceedings 868, 46 (27);.63/ Queueing system analysis of multi server model at XYZ insurance company in Tasikmalaya city AIP Conference Proceedings 868, 44 (27);.63/ Can goal-free problems facilitating students flexible thinking? AIP Conference Proceedings 868, 5 (27);.63/ Problem based learning to improve proportional reasoning of students in mathematics learning AIP Conference Proceedings 868, 52 (27);.63/ Preface: 4th International Conference on Research, Implementation, and Education of Mathematics and Sciences (ICRIEMS) AIP Conference Proceedings 868, (27);.63/

2 Classification of Toddler Nutritional Status Using Fuzzy Inference System (FIS) Dian Permatasari, a), Isnaini Nur Azizah, b), c), Hanifah Latifah Hadiat 2, d) Agus Maman Abadi Graduate Program of Mathematics Education, Yogyakarta State University, Indonesia 2 Mathematics Department, Faculty of Mathematics and Science, Yogyakarta State University, Indonesia a) Corresponding author: dian.permatasari75@gmail.com b) isnaininurazizah25@gmail.com c) hanifahlhadiat@gmail.com d) agusmaman@uny.ac.id Abstract. Nutrition is a major health problem and concern for parents when it is relating with their toddler. The nutritional status is an expression of the state caused by the status of the balance between the number of intake of nutrients and the amount needed by the body for a variety of biological functions. The indicators that often used to determine the nutritional status is the combination of Weight (W) and Height (H) symbolized by W/H, because it describe a sensitive and specific nutritional status. This study aims to apply the Fuzzy Inference System Mamdani method to classify the nutritional status of toddler. The inputs are weight and height of the toddler. There are nine rules that used and the output is nutritional status classification consisting of four criteria: stunting, wasting, normal, and overweight. Fuzzy Inference System that be used is Mamdani method and the defuzzification use Centroid Method. The result of this study is compared with Assessment Anthropometric Standard of Toddler Nutritional Status by Ministry of Health. The accuracy level of this fuzzy model is about 84%. INTRODUCTION Nutrition is an important role in the human life cycle from conception until old. Health Act of 29 mentions that the main priority of nutrition improvement program in Indonesia is vulnerable groups that are infants and toddlers. Malnutrition in infants and toddlers may interfere the growth and development and continue into adulthood if not treated in early stage []. Therefore, parents need to check their toddler s nutritional status regularly. Nutritional status is an expression that state the equilibrium in the form of specific variables or embodiment of nutrients in the form of certain variable [2]. In the other hand, nutritional status is an integral part of the overall health of an individual and provides an indicator of the well-being of children living in a particular region [3]. Nutritional status is influenced by many reasons, such as food intake [4], the amount of physical activity [5], the economic conditions [6,7], and others. Nutritional status assessment is required to determine the health condition, identify the essential nutrients in certain populations or groups that have lack, develop an effective public health policy and prevent or cure diseases related to malnutrition and obesity [8]. Malnutrition and obesity happen because there is a lack of balance between nutrient intakes with the nutritional needs of the body. Toddlers who have a nutritional status are obesity will be at risk of various health problems such as diabetes, high blood pressure, and cholesterol. Obesity is the accumulation of excess overweight on the body and it causes serious long-term consequences to health. It is a leading cause of preventable deaths in the world [9]. On the other hand, malnutrition is associated with more than half of all child deaths worldwide [6] and have a low cognitive development as well as serious health problems later in life which reduces the quality of life of individuals []. The 4th International Conference on Research, Implementation, and Education of Mathematics and Science (4th ICRIEMS) AIP Conf. Proc. 868, ; doi:.63/ Published by AIP Publishing /$3. 47-

3 Currently, Indonesia is one of 7 countries that have three nutritional problems in toddlers that is stunting, wasting, and overweight were reported in the Global Nutrition Report (GNR) 25 Nutrition Country Profile Indonesia. The prevalence of three malnutrition is 36.4% stunting, 3,5% wasting, and,5% overweight []. Thus, it is very important to know the nutritional status of toddler. Knowing the nutritional status of toddler has broad implications for the better development of future generations []. Nutrition assessment includes taking anthropometric measurements and collecting information about a client s medical history, clinical and biochemical characteristics, dietary practices, current treatment, and food security situation [2]. Nutritional status can be measured using anthropometric measurements. There are three kind indicators that often used to measure it, that is Weight according to Age that is symbolized by W/A, Height according to Age symbolized by H/A, and the combination of Weight and Height symbolized by W/H [3]. W/A indicator represents current nutritional status (when measured) sensitively because it is easy to change and W/A indicator is not specific because weight is influenced by age and height. H/A indicator is describe the nutritional status in the past, while W/H indicator is sensitive and specific nutritional status at this time [4]. Weight is correlated linearly with height; it means that the increase of weight will be followed by the increase of height. Therefore, in this study, the criteria used in determining the nutritional status are the combination of weight and height or W/H. There are four criteria for nutritional status based on weight according to height that is overweight, normal, wasting, and stunting [3]. Here is a Nutritional Status Classification Toddler from Assessment Anthropometric Standard of Toddler Nutritional Status by Ministry of Health. TABLE.Guidelines for the classification of toddler nutritional status [3] Indicator Nutritional Status Criteria The combination of Overweight > +2 SD weight and height Normal -2 SD until +2 SD (W/H) Wasting -2 SD until -3 SD Stunting < -3 SD where SD: Standard Deviation. In determining nutritional status of children, it often occurs inaccuracy. Because of it, it needs fuzzy set theory that can be used to help some complex and uncertain issue to get better accuracy. Fuzzy Inference System (FIS) has been used extensively to solve various problems. Earlier, in mining, FIS has been used to determine the existence of hydrocarbon prospective zone through a qualitative analysis in a resevoir [5]. FIS also has been used to classify the level of countries region economic development [6]. In medical, FIS has been used to determine malnourished toddlers [7] and the adults nutritional status [8]. A fuzzy set is to represent and manipulate uncertain data, that it is vague that is introduced by Zadeh in 965 [9]. Fuzzy logic is a mathematical discipline that we use every day and helps us to reach the structure in which we interpret our own behaviors. Its basis is formed by true and false values and Fuzzy Set Theory in which the values between partially true, partially false are determined [2]. Generally, fuzzy inference systems (FIS) are systems based on knowledge or rule. That is, in the basis of a FIS lie the if-then rules [2]. Therefore, classification of toddler nutritional status needs fuzzy inference system that can know the nutritional status of children in terms of weight and height. Because of the lack of FIS to determine the nutritional status of children, the study aims to classify the nutritional status of children using the Fuzzy Inference System. MATERIAL AND METHOD The data used in this study are 4 data of toddler s weighing from Puskesmas Kecamatan Belakang Padang [22]. Determination of the toddler nutritional status is using Fuzzy Inference System (FIS) with Mamdani method. Mamdani method is also known by the name of Min-Max method. It means find the minimum value of each rule and then find the maximum value of the combined consequences of any such rules. Mamdani method is suitable to use when the inputs are received from a human not a machine. The steps used to classification of toddler nutritional status using fuzzy logic are in the below. 47-2

4 Fuzzy Rule Base Crips value Fuzzification Fuzzy Inference System Defuzzification Fuzzy Values Fuzzy Values FIGURE. Fuzzy Inference system with fuzzification and defuzzification [23] Crips value After deciding on designing a fuzzy system the first step to follow is to collect the rules of if-then. These rules are generally collected according to Assessment Anthropometric Standard of Toddler Nutritional Status by Ministry of Health. As it is seen in Fig., in FIS model the input and output values of the system are crisp values. By fuzzification these crisp input values, its fuzzy membership values and degrees are obtained. These obtained fuzzy values are processed in Fuzzy Inference System (FIS). Here, the fuzzy output values which are also obtained by using rule-base are sending to the defuzzification unit, and from this unit the final crisp values are obtained as output [23]. RESULT AND DISCUSSION In this study, the data that be used is 4 data of toddler s weighing from Puskesmas Kecamatan Belakang Padang [22]. TABLE 2. Toddler s weighing data No Posyandu s Age Weight Height Weighing Date Name Birth Date Gender Name (Month) (Kg) (cm) Kemuning 25-Mar-23 Indah Yani 26//2 PR 37, Kemuning 25-Mar-23 Rohmansyah 28/5/2 LK 32 3, Melor -Mar-23 Desi Amelia 25/4/28 PR 57 7,5 Based on the data in Table 2, the input variables are Weight (W) and Height (H), while the output variable is the Nutritional Status (NS). The universal set of inputs and outputs are described in Table 3. TABLE 3. The universal set of the input and output variable Function Variable s Name Universal Set Input Weight [5,35] Height [6,2] Output Nutritional Status [-4, 4] Fuzzy set is characterized by its membership functions. It classifies the element in the set, whether it is discrete or continuous. If X is a collection of objects denoted by x, then fuzzy set A in X is a set of sequential pairs A {( x, A( x) x X )} () where A ( x) is a degree of membership of x in A, it is a function that maps X into the interval [,]. Fuzzy number can be defined as fuzzy set in real number set R with some condition [24]. The membership functions can also be formed by graphical representations. The graphical representations may include different shapes [25]. Thus, in this study, there are two membership functions that be used in this study that are triangular membership function and trapezoidal fuzzy numbers. The input variable is Weight and Height of the toddler. The first input is Weight. Weight variable is symbolized by W. There are three fuzzy values of Weight that are Light (L), Normal (N), and Heavy (H). The membership function set defined Weight variable are as follows. ;if x 2 x W L ( x) ;if x 2 (2) ;if x

5 W N x ;if x 2 ( x) 3 x ;if 2 x 3 ;if x or x 3 ;if x 3 x 2 x W H ( x ) ;if 2 3 ;if x 2 Membership functions of three fuzzy values of Weight (W) are shown in below. (3) (4) FIGURE 2.Membership function of three fuzzy values of Weight where the horizontal axis is the input value that is weight variable, while the vertical axis is the membership degree of the input value of weight variable. The second input is Height. Height variable is symbolized by H. There are three fuzzy values of Height, that are Short (S), Normal (N), and Tall (T). The membership function set defined Height variable are as follows. ;if x2 7 7 x H S ( x2) ;if 7 x2 9 2 ;if x2 9 (5) x 7 2 ;if 7 x2 9 x H N ( x2) 2 ;if 9 x2 (6) ;if x2 7 or x2 ;if x2 x H T ( x2) ;if 9 x2 2 ;if x2 9 (7) Membership functions of three fuzzy values of Height (H) are shown in below. 47-4

6 FIGURE 3. Membership function of three fuzzy values of Height where the horizontal axis is the input value of the height variable, while the vertical axis is the membership degree of the input value of height variable. The output of this fuzzy inference system is Nutritional Status. The fuzzy set of nutritional status is obtained based on the Assessment Anthropometric Standard of Toddler Nutritional Status assigned the Ministry of Health. The nutritional status variable is symbolized by NS. There are four fuzzy values of Nutritional Status, that are Stunting (S), Wasting (W), Normal (N), and Overweight (O). The membership function set defined Nutritional Status variable are as follows. ;if 4 x x NS S ( x) ;if 3.5 x 2.5 (8).5 ;if x 2.5 x ;if 3.5 x 2.75 ;if 2.75 x 2.25 NS W ( x) (9).5 x ;if 2.25 x.5.75 ;if x.5 or x 3.5 x 2.5 ;if 2.5 x.5 ;if.5 x NS N ( x) 3 x () ;if x 3 2 ;if x 3 or x 2.5 ;if 3 x 4 x 3 NS O ( x) ;if x 3 () 2 ;if x Membership functions of three fuzzy values of Nutritional Status (NS) are shown in below. 47-5

7 FIGURE 4. Membership function of three fuzzy values of Nutritional Status The general structure of the developed FIS is shown in below. FIGURE 5. The general structure of the FIS developed Establishment of the rule using the membership function that has been defined previously. After identify the membership function, the next step to follow is to collect the fuzzy rules. Rules are created to express the relation between input and output. These rules are generally collected according to Assessment Anthropometric Standard of Toddler Nutritional Status by Ministry of Health. The fuzzy rule-based system uses IF THEN rule-based system [2]. Therefore, the mapping between input-output is the IF-THEN and the operator is used to connect between two inputs is the AND operator, and. In this study, there are some rules shown in below. TABLE 4. Fuzzy rule of classification of toddler nutritional status Indicator Weight Light Normal Heavy Height Short Normal Overweight Overweight Normal Wasted Normal Overweight Tall Stunting Wasted Normal [R] : If the weight is light and the height is short then the nutritional status is normal. [R2] : If the weight is light and the height is normal then the nutritional status is wasting. [R3] : If the weight is light and the height is tall then the nutritional status is stunting. [R4] : If the weight is normal and the height is short then the nutritional status is overweight. [R5] : If the weight is normal and the height is normal then the nutritional status is normal. [R6] : If the weight is normal and the height is tall then the nutritional status is wasting. [R7] : If the weight is heavy and height is short then the nutritional status is overweight. [R8] : If the weight is heavy and height is normal then the nutritional status is overweight. [R9] : If the weight is heavy and height is tall then the nutritional status is normal. After building the rule, the next step is fuzzification. This study use fuzzification with Mamdani method with MAX-MIN rules, that is take the maximum value of the results of the implications min then use it to modify the fuzzy 47-6

8 areas, and apply it to output by using OR operator (union). The validity degrees (α) for each rule according to Mamdani max-min rule are shown with the formulas below. min(light( x ),short( x )) 2 min(light( x ),normal( x )) min(heavy( x),normal( x2)) min(heavy( x ), tall( x )) 9 2 The maximum of the validity degrees of the triggered rules are calculated with the formulas below.,2,...,9 max(, 2,..., 8, 9) In the defuzzification process, the exact expression is obtained with centroid method according to a validity degree [2]. The defuzzification process is defined as a mapping from the output of the fuzzy inference engine to crisp point [23]. The output value (the nutritional status is found as.763 in normal criteria) with respect to the input values (the weight is 5 kg and the height is cm) obtained from the designed FIS is shown as an example in Fig. 6, whereas according to Assessment Anthropometric Standard of Toddler Nutritional Status the toddler is in Normal criteria. FIGURE 6. The Nutritional Status obtained according to the obtained Weight and Height The nutritional Status related to both Weight and Height can be seen in Fig. 7. FIGURE 7. The change in the nutritional status regarding to weight and height 47-7

9 After getting all the result from Fuzzy Inference System (FIS), then it is compared with Standard Anthropometric Nutrition Status Assessment (SANSA) established by the Ministry of Health to determine the accuracy level. The following table is presented along with each pair of input and output of the FIS output by Standard Anthropometric Nutrition Status Assessment (SANSA) established by the Ministry of Health. Suppose * y as the output results of calculations using the FIS. TABLE 5. The comparison of FIS output and Standard Anthropometric Nutritional Status Assessment (SANSA) No. Input Input 2 Output (y*) FIS Output SANSA Conclusion,7 95 -,4727 Normal Normal Correct 2 3,5 96 -,9 Normal Normal Correct 4 7,4-2,66366 Wasting Normal Incorrect Based on Table 5, the accuracy level of the Fuzzy Inference System Mamdani method applied in determining the nutritional status is as follows. Total of correct data Accuracy level % (2) Total data Thus, the accuracy level of the Fuzzy Inference System with Mamdani method to classification of toddler nutritional status is 84%. In this study, FIS is designed to help the Posyandu employee to determine the nutritional status to the toddler. Results showed that proposed FIS could be used to determine the nutritional status, which is a complex and uncertain issue, and can get good results. CONCLUSION Implementation of Fuzzy Inference System with Mamdani Method can be used to classify the toddler nutritional status. The input of this fuzzy system is weight and height of toddler and the output of this fuzzy system is the classification of nutritional status of toddlers, which consists of four criteria that is stunting, wasting, normal, and overweight. There are nine rules used. Fuzzy Inference System uses Mamdani method and defuzzification uses Centroid Method. To determine the accuracy level, the result of this fuzzy system is compared with the classification based on the Anthropometric Standards for Assessment of the Nutrition Status of Children designated by the Ministry of Health. The accuracy level obtained in the classification of Toddler Nutrition Status is 84%. REFERENCES. Kemenkes, Undang-undang Republik Indonesia No. 36 Tahun 29 Tentang Kesehatan (3 Oktober 29). 2. I. D. Supariasa, B. Bakri, and I. Fajar, Penilaian Status Gizi (EGC, Jakarta, 22). 3. J.A. Adegun, O.B. Ajayi Vincent, and E.O.Alebiosu, European Scientific Journal, Vol. 9 (7), (23). 4. P. Niblett, Statistics on Obesity, Physical Activity and Diet (Health and Social Care Information Centre, England, 26), pp H. Dinsdale, C. Ridler, and L. Ells, A Simple Guide to Classifying Body Mass Index in Children (National Obesity Observatory, Oxford, 2), pp S. Chakrabarty and P. Bharati, Italian Journal of Public Health, Vol. 7 (3),33 3 (2). 7. R. F. Putri, D. Sulastri, dan Y. Lestari, Jurnal Kesehatan Andalas, Vol. 4 (), (25). 8. I. Elmadfa and A. L. Meyer, Advances in Nutrition., Vol. 5 (5), 59S 598S (24). 9. U. Umoh and E. Isong, Computer Engineering and Intelligent Systems, Vol. 6 (9), 2 33 (25).. A. Srivastava, S. E. Mahmood, P. M. Srivastava, V. P. Shrotriya, and B. Kumar, Archieves of Public Health, Vol 7 (8), 2 9 (22).. IFPRI, Global Nutrition Report 26: From Promise to Impact: Ending Malnutrition by 23 (IFRI, Washington, 26). 2. FANTA, Nutrition Assessment, Counseling, and Support (NACS): A User s Guide Module 2: Nutrition Assessment and Classification (FANTA, Washington, 26), pp Kemenkes, Keputusan Menteri Kesehatan RI No.995/Menkes/SK/XII/2 Tentang Standar Antropometri Penilaian Status Gizi Anak (3 Desember 2). 4. Soekirman, Ilmu Gizi dan Aplikasinya untuk Keluarga dan Masyarakat (Dirjen Dikti, Jakarta, 2). 47-8

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