Journal of Information Technology and Computer Science Volume 7, Number 3, December 2022, pp. 172-182
Journal Homepage: www.jitecs.ub.ac.id
Early Stunting Detection System for Toddlers Based on Height and Weight Using Backpropagation Neural
Network Method
Dini Eka Ristanti*1, Dahnial Syauqy2, Barlian Henryranu Prasetio3
1,2,3Brawijaya University, Malang
[email protected]1, [email protected]2, [email protected]3
*Corresponding Author
Received 28 December 2021; accepted 26 December 2022
Abstract. Stunting is a chronic nutritional problem characterized by height and weight problems. Toddlers who have height and weight of more than minus 2 standard deviations are at risk of suffering from stunting and require monitoring for 3 to 6 consecutive months. Currently, the system still measures toddlers' height and weight, then matches it with the World Health Organization (WHO) growth data table. Therefore, we proposed to develop a system to detect height and weight as well as the risk of stunting in toddlers using an ultrasonic sensor, load cell, and backpropagation algorithm. In its implementation, the ultrasonic sensor achieves an accuracy of 99%, and the load cell reaches 93%. The system uses backpropagation neural network method, which achieved an R of 0.99845 using 3 inputs, 16 hidden layers, 1 layer for re-weighting, and 1 output layer.
The mean squared error reaches 0.01 with 2 prediction classes, low risk, and high risk stunting. Overall, the total system accuracy can reach 97.75%.
Keywords: stunting, toddlers, embedded system, backpropagation
1 Introduction
Stunting is a chronic nutritional problem in toddlers that occurs chronically starting from the first 1000 days of life [1]. Stunting is characterized by a height that is shorter than the aged standard and having a height of more than minus 2 standard deviations in WHO growth table [2]. Children with a history of low birth weight, and children with normal birth weight who are malnourished have the potential to suffer from stunting [3]. Early detection of stunting symptoms is important to improve nutrition, sanitation, and parental knowledge. Providing education to parents is very important to emphasize the risk of severe stunting that is not detected and provide appropriate treatment [4].
Nowadays, the method used in hospitals to predict stunting is being done using a manual approach. The nurse must check the height and weight first, then compare the result with the graphic and data table provided by WHO. The process of predicting stunting case on a child takes time, and usually confused to match the height and weight to get an accurate result [5]. Besides, toddlers usually cried when they have to measure their height and weight [6]. Based on the problems that have been described, we proposed a system that can measure the weight and height of toddlers, followed by determining the risk of stunting using the backpropagation neural network method. The design of the scales is also considered to be in the form of a funny animal that is familiar to toddlers, thus minimizing the risk of toddlers crying when taking measurements.
Research related to the system of measuring height and weight has been widely
Dini Eka., et al. , Early Stunting Detection System... 173 developed. Sensors that are widely used in research are ultrasonic and load cell and HX711 module [5]. In the previous study, the reading ability of the ultrasonic sensor and load cell has an error below 1% so can be said accurately. The next research was conducted with the research object of toddlers aged 0-3 years. The study used ultrasonic and load cells to get the height and weight of toddlers. Research can run optimally using both types of sensors, and the weight and height of toddlers can be determined [6]. To determine the risk of stunting in toddlers, it is necessary to implement a method that can determine whether toddlers are at risk or not. The method used is more suitable for supervised learning by classification or prediction. In this study, predictions were implemented using a backpropagation neural network because its reliability in predicting based on previous data was quite high [7]. In a previous study, backpropagation neural network was used and compared with LVQ for early detection of coronary heart disease. The variables used for input features in the study were age, gender, cholesterol, pulse rate, shortness of breath, systolic blood pressure, triglycerides, blood sugar, chest pain, and cough. From the results, it was found that the Backpropagation method was better than the LVQ method. The training accuracy for Backpropagation was 95.99097% compared to 66.89659% for LVQ and a testing accuracy for Backpropagation was 68.76034% compared to 54.30313% for LVQ [8].
In another study, an automation system to detect diabetes mellitus and dehydration levels to provide information to patients had been proposed. Urine color, ammonia levels and pH of urine were used to determine diabetes mellitus and dehydration levels and were obtained from the readings of the TCS3200 color sensor, MQ135 gas sensor, and pH sensor respectively and then classified using Artificial Neural Network method.
The experimental test showed an accuracy rate of 80% with an average computing time of 2.03 seconds [9]. The next research showed that using backpropagation neural network could achieve the lowest mean squared error (MSE) {0,02815; 0,01686;
0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer [11].
Another study examining the spread of DHF stated that the use of backpropagation artificial neural networks using a learning rate of 0.4 with an MSE of 0.0099 can be concluded that the use of backpropagation has a high accuracy of 90%. The data used is 82 data, with 62 data as training data and 20 data as test data. Data on average temperature, rainfall, number of rainy days, population density and larvae-free rate (ABJ) are input data while the target or output data are DHF case data in Ambon City [12].
From the existing research, it can be concluded that the backpropagation neural network method is good enough to be applied to forecasting or prediction. This method works like the human brain by training on existing data. Data processing is carried out on several hidden layers which have been determined with available input and output [10]. The training was continued by entering the data used as test data to find out whether the training used was sufficient or not.
This research uses an ultrasonic sensor, load cell, HX711 module, and backpropagation neural network according to previous analysis. The expected result of this study is to be able to display the height and weight of toddlers as well as the risk of stunting by WHO’s (World Health Organization) growth table.
174 JITeCS Volume 7, Number 3, December 2022, pp 172-182
2 Method
2.1. Literature review
At this stage, there are several literature studies to support the research. Information collection is obtained through references such as journals, books, previous research results, and other reliable sources. Some of the literature sources used in this study are related to stunting, backpropagation neural network, and general embedded system.
2.2. Data collection
To support the course of research, research was conducted at the Children's Poly and Dahlia Room RSUD dr. Soedomo Trenggalek. The research was conducted on toddlers aged 12-59 months who are taken randomly without regard to gender. This research uses height and weight data on toddlers to be tested on the system. Due to the limited number of toddlers undergoing treatment at that time pandemic, only 16 toddlers were selected.
2.3. Proposed model
Block diagram 1 shows that the system has 3 main categories namely input, process, and output. The first process is doing the measurement of sensors, then displayed on the first output. At the second input, the user has to choose the toddler’s age, and will be processed again in the second process to get the result. The three main categories are then integrated into a unified system.
Figure 1. System Block Diagram
In block diagrams and flowcharts, there are inputs from the ultrasonic sensor and load cell. The input is then processed by conversion process, so that it is displayed in the form of height in cm and weight in kg.
From the flowchart in Figure 2, ultrasonic data must be converted to cm by multiplying by 0.034/2. The ultrasonic sensor worked by using distance multiplied by the speed of sound and time. The speed of sound is 340 m/s or 0.034 µs, and sound waves have 2 times the travel time. Due to the condition above, we can obtain the final formula as in equation 1
𝑠 =0.034
2 (1)
After getting the result of distance measurement or s, further processing is carried out to get the result of the toddler’s height. The maximum height based on the table is 120 cm, and the ultrasonic sensor is 3 cm height, so the tools are paired at 125 cm. The maximum height that has been determined is then subtracted by the ultrasonic result to obtain the formula in equation 2
ℎ𝑒𝑖𝑔ℎ𝑡 = 120 − 𝑠 (2)
Dini Eka., et al. , Early Stunting Detection System... 175
Figure. 2 Overall System Flowchart
After converting the ultrasonic sensor as shown in Fig. 2., the sensor reading results are obtained as in Table 1.
Table 1 Input Ultrasonic Reading Raw Data Conversion Result (cm)
1235 99
1000 103
647 109
2058 85
529 111
Figure. 1. Load Cell Conversion Flowchart
After the conversion as shown in Fig. 3., the sensor reading result is obtained as in Table 2.
176 JITeCS Volume 7, Number 3, December 2022, pp 172-182 Table 2 Input Ultrasonic Reading
Raw Data Conversion Result (kg)
3398 2
8297 8
47221 47
13478 13
2086 2
The conversion results are then displayed on the LCD. A user must enter the age of the toddler by the available button. After getting 3 inputs from the height, weight, and age of toddlers, the next process was applying a backpropagation algorithm using Arduino Uno. The result of the backpropagation algorithm will be shown in high-risk or low-risk stunting classes and will be displayed on LCD.
The result of sensor conversion was then normalized using excel with a range of 0- 1. After normalization, the training data was then processed using Matlab with the nntool toolbox to get optimal network training. Network training is then followed by implementing an algorithm into Arduino Uno according to epoch and mean squared error (MSE) using the neural network library in Arduino IDE.
2.5. Experimental setup and evaluation method
The test was carried out by comparing the measurement with existing weight gauges at RSUD dr. Soedomo Trenggalek. To get measurement results, toddlers must take measurements three times. Two measurements on the system, and one measurement on the hospital scale. Two measurements on the system aim to get the precision value of the system, and one measurement on the hospital scales aims to get accurate results based on hospital scales.
3 Result and Discussion
3.1. System Design
Figure. 2 System Schematic Diagram
In Fig. 4 there is a schematic diagram that explains Arduino Uno’s connection with sensors and LCD. Arduino is connected to the ultrasonic sensor with a height of 120 cm by D9 and D10 pins. On D4 and D5 pins, Arduino is also connected to the HX711 module which is also connected to a load cell with a maximum weight of 50 kg by E-, E+, and A- pins. After getting height and weight data, the data will be shown on an
Dini Eka., et al. , Early Stunting Detection System... 177 LCD. Then the user must enter the age of toddlers by clicking the button available.
Then height, weight, and age data are processed on Arduino using a backpropagation neural network and generate 2 prediction classes.
The system is designed with 3 main parts that are interconnected. The first part is a scale with a square shape measuring 25×25 cm, the second part is a height measuring 125 cm, and the last part is a box for processing and LCD. The main material used in this system is 3 mm acrylic as shown below. The system has a maximum limit for measuring height, which is 120 cm, and a maximum limit for measuring weight, which is 50 kg.
Figure. 3. System Design
3.2. Data Normalization
Training data needs to be processed before network training is carried out. The processing used in training data is normalization. Normalization aims to change the value of a numeric column in toddler height and weight data, so the data are on the same scale, in the range of 0-1. Range 0-1 was chosen because the activation function used is binary sigmoid which has range 0-1. Range nearly 0 means low risk and nearly 1 means high risk of stunting. This equation is added by 0.1 to give a minimum value of 0.1, so there is no 0 in the result.
To normalize data, using min-max normalization shown in equation 3𝑋𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = (𝑋−𝑋𝑚𝑖𝑛)
(𝑋𝑚𝑎𝑥−𝑋𝑚𝑖𝑛)+ 0.1 (3)
The comparison of raw and normalized data will be shown in Table 3.
Table 3 The comparison between raw and normalized data
Raw Data Normalized Data
Height (cm)
Weight (kg)
Age
(years) Risk Height (cm)
Weight (kg)
Age
(years) Risk
85 11.5 2 0 0.248 0.370 0.367 0
107 17 4 0 0.9 0.9 0.9 0
90 8.7 2 0 0.396 0.1 0.367 0
86 12 3 1 0.278 0.418 0.633 1
80 10 2 0 0.1 0.225 0.367 0
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3.3. Backpropagation Neural Network Design
The backpropagation neural network was designed using Matlab software. The normalized data is then used for data training and processed using the tool provided by Matlab. At the network training stage, R results obtained are 0.99845 with 16 hidden layers. This training takes 8 seconds with 15000 epochs reaching 11709 iterations.
Figure. 4 Network Training Result 3.4. Ultrasonic Sensor Test
Ultrasonic sensor tests were aimed to determine the performance and suitability of sensor readings. The test was carried out by comparing the measurement with the existing height measuring device at RSUD dr. Soedomo. To get measurement results, toddlers must take measurements three times. Two measurements on the system, and one measurement on the hospital scale. Two measurements on the system aim to get the precision value of the system, and one measurement on the hospital scales aims to get accurate results based on hospital scales.
To determine the accuracy and precision, calculations are carried out using the Mean Absolute Percentage Error (MAPE) formula for accuracy and Mean Relative Error (MRE) for precision. The result will be shown in Table 4.
Table 4 Ultrasonic Test Result SH I
(cm) SH II
(cm) HH (cm) Accuracy Precision
85 85 85 100% 100%
108 109 107 99% 99%
85 86 86 99% 99%
92 90 90 99% 99%
80 81 80 99% 99%
99% 99%
Description
SH I: System Height Measurement on the first try SH II: System Height Measurement on the second try HH: Hospital Height Measurement
Dini Eka., et al. , Early Stunting Detection System... 179
3.5. Load Cell Sensor Test
Load cell and HX711 test was aims to determine the performance and suitability of sensor readings. The test was carried out by comparing the measurement with existing weight gauges at RSUD dr. Soedomo Trenggalek. To get measurement results, toddlers must take measurements three times. Two measurements on the system, and one measurement on the hospital scale. Two measurements on the system aim to get the precision value of the system, and one measurement on the hospital scales aims to get accurate results based on hospital scales.
To determine accuracy and precision, calculations are carried out using the Mean Absolute Percentage Error (MAPE) formula for accuracy and Mean Relative Error (MRE) for precision. The result will be shown in Table 5.
Table 5 Load Cell Test Result SW I
(kg)
SW II
(kg) HW (kg) Accuracy Precision
12 11 11.5 86% 99%
19 16 17 92% 99%
9 10 8.7 91% 99%
11 13 12 92% 98%
10 11 10 95% 99%
93% 98.8%
Description
SW I: System Weight Measurement on the first try SW II: System Weight Measurement on the second try HW: Hospital Weight Measurement
3.6. Backpropagation Neural Network Test
A backpropagation neural network test is used to determine the performance of prediction using this algorithm. The result obtained from the system was then confirmed by health workers who checked based on the WHO nutrition table so the result was obtained in Table 6. The test results that have been calculated for accuracy get 100%.
Table 6 Backpropagation Test Result Height
(cm) Weight
(kg) Age
(years) Result Diagnose Accuracy
100 11 3 Low Low 100%
99 16 4 Low Low 100%
94 10 4 Low Low 100%
84 13 3 High High 100%
3.7. Overall System Test
This test aims to determine overall system performance. This test used the same comparison as testing on sensors and health workers diagnose the risks. The overall system test will be shown in Table 7.
180 JITeCS Volume 7, Number 3, December 2022, pp 172-182
Table 7 Overall System Test Result HH
(cm) SH
(cm) HW
(kg) SW
(kg) A
(years) R D A
99 100 14 14 3 Low Low 98%
99 99 17 17 4 Low Low 100%
95 94 16 17 4 Low Low 95%
85 84 12 12 3 High High 98%
97.75%
Description
HH: Hospital Height Measurement SH: System Height Measurement HW: Hospital Weight Measurement SW: System Weight Measurement A: Toddlers Age
R: Stunting Risk Shown by System D: Health Worker Diagnose A: Accuracy
4 Conclusion
The system has applied a backpropagation neural network algorithm in predicting the condition of toddlers. From the results of the design and implementation that have been done, there are several conclusions that can be drawn. Accuracy and precision carried out by comparing the system with the equipment owned by RSUD dr. Soedomo Trenggalek. From the tests that have been carried out on 5 toddlers obtained the accuracy of the ultrasonic sensor is 99% and the precision is 99%, while the load cell obtained accuracy on the sensor of 93% and precision of 98.8%. The Performance testing of classification algorithm was done by taking several training data and training targets that have been confirmed by health workers. The overall system testing process was carried out from the determination process height using ultrasonic sensor, weight using load cell, and determine the risk of stunting with the backpropagation algorithm, which obtained an accuracy of 97.75%. Based on the result, we notice that the system was able to provide support to give early prediction in detecting stunting for toddlers.
In further system development, nutritional status can be added since nutritional status contributes to stunting.
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