Combination of Decision Support Systems and Geographic Information Systems in Determining Undernutrition Status Using Deep Learning And K-Means Clustering

Mohammad Ilham Bahri; Muhammad Aswin; Mohammad Fauzan Purnomo

doi:10.21107/ijseit.v7i02.22606

Combination of Decision Support Systems and Geographic Information Systems in Determining Undernutrition Status Using Deep Learning And K-Means Clustering

Mohammad Ilham Bahri, Muhammad Aswin, Mohammad Fauzan Purnomo

Abstract

The nutritional status of toddlers is a factor that needs to be considered in maintaining their health because toddlerhood is a developmental period that is vulnerable to nutrition. Death cases in toddlers are one factor in the lack of monitoring by local governments. It is necessary to carry out activities to observe and anticipate problems to take action as early as possible. This problem can be solved using the help of Decision Support Systems and Geographic Information Systems. Machine learning-based deep learning methods are employed as alternative algorithms for Decision Support Systems. Deep learning is considered a very promising approach due to its ability to analyze and extract patterns from data, afterwards applying these patterns to address following challenges. The selection of a Geographic Information Systems was conducted by employing the k-means clustering technique in order to create a visual representation of the zone groupings within each region. The accuracy of the deep learning method for determining nutritional status was found to be 95.24%. In the context of mapping regional zone groupings, it is noteworthy that the k-means clustering method exhibits a remarkable accuracy rate of 100% in relation to the true value. Based on the obtained findings, it can be inferred that the deep learning and k-means clustering techniques exhibit a high level of accuracy in discerning nutritional status and delineating regional zone groupings.

Keywords

Deep Learning, K-Means Clustering, Decision Support System, Determining of Nutrition Status, Mapping, Geographical Information Systems

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References

S. Taki, “Malnutrition among children in Indonesia: It is still a problem,” J. Kedokt. dan Kesehat. Indones., vol. 9, no. 2, pp. 68–71, 2018, doi: 10.20885/jkki.vol9.iss2.art1.

C. Hayat and B. Abian, “The modeling of artificial neural network of early diagnosis for malnutrition with backpropagation method,” Proc. 3rd Int. Conf. Informatics Comput. ICIC 2018, 2018, doi: 10.1109/IAC.2018.8780505.

M. M. Mottalib, M. M. Rahman, M. T. Md. Tarekx, and F. Ahmed, “Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System,” Trans. Mach. Learn. Artif. Intell., vol. 4, no. 4, pp. 1–8, 2016, doi: 10.14738/tmlai.44.1962.

O. Rabie, D. Alghazzawi, J. Asghar, F. K. Saddozai, and M. Z. Asghar, “A Decision Support System for Diagnosing Diabetes Using Deep Neural Network,” Front. Public Heal., vol. 10, no. March, pp. 1–13, 2022, doi: 10.3389/fpubh.2022.861062.

L. Aulck, D. Nambi, and J. West, “Increasing Enrollment by Optimizing Scholarship Allocations Using Machine Learning and Genetic Algorithms,” Edm2020, no. Edm, pp. 29–38, 2020.

G. Giray, K. E. Bennin, Ö. Köksal, Ö. Babur, and B. Tekinerdogan, “On the use of deep learning in software defect prediction,” J. Syst. Softw., vol. 195, p. 111537, 2023, doi: 10.1016/j.jss.2022.111537.

A. Erwin, M. A. Hanafiah, F. I. Komputer, and U. P. I. Y. Padang, “Perceptron Neural Network Penentuan Status Gizi Anak Berbasis Web,” vol. 15, pp. 43–53, 2022.

S. Kar, S. Pratihar, S. Nayak, S. Bal, H. L. Gururaj, and V. Ravikumar, “Prediction of Child Malnutrition using Machine Learning,” IEMECON 2021 - 10th Int. Conf. Internet Everything, Microw. Eng. Commun. Networks, 2021, doi: 10.1109/IEMECON53809.2021.9689083.

H. R. Pourghasemi et al., “Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020),” Int. J. Infect. Dis., vol. 98, pp. 90–108, 2020, doi: 10.1016/j.ijid.2020.06.058.

F. Virgantari and Y. E. Faridhan, “in Indonesia ’ s Provinces,” vol. 5, no. 2, pp. 1–7, 2020.

Z. K. Chowdhury and D. Sinha, “Development of a decision support system for CECs,” ACE 2013 - 2013 AWWA Annu. Conf. Expo., pp. 709–712, 2013.

M. Qjidaa et al., “Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images,” 2020 Int. Conf. Intell. Syst. Comput. Vision, ISCV 2020, pp. 1–6, 2020, doi: 10.1109/ISCV49265.2020.9204282.

P. Guo, Z. Ye, K. Xiao, and W. Zhu, “Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 10, pp. 5037–5050, 2020, doi: 10.1109/TKDE.2020.3047894.

K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.

M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking,” IEEE Access, vol. 8, pp. 90847–90861, 2020, doi: 10.1109/ACCESS.2020.2994222.

G. Jiang, Y. Tian, and C. Xiao, “GIS-based rainfall-triggered landslide warning and forecasting model of Shenzhen,” Int. Conf. Geoinformatics, 2013, doi: 10.1109/Geoinformatics.2013.6626026.