Performance Analysis for Classification of Malnourished Toddlers Using K-Nearest Neighbor
(1) Program Studi Magister Informatika, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
(2) Program Studi Teknik Elektro, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
(3) Program Studi Magister Informatika, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Abstract
Purpose: Malnutrition in toddlers is a nutritional issue that Indonesia is still dealing with. Toddlers can suffer from decreasing cognitive and physical abilities, as well as being categorized as having a high risk of death. Early detection is crucial for preventing this and providing appropriate treatment if malnutrition is detected. Classification is a machine-learning technique widely used in disease detection. Because it is simple and easy to implement, K-Nearest Neighbor is the most used classification algorithm. Detecting malnutrition can be done automatically and more quickly by utilizing classification and machine learning algorithms. The aim of this study was to analyze performance to find out which model is best for detecting malnutrition by evaluating the performance of classification using KNN with the Euclidean distance function.
Methods: The dataset used in this study is the nutritional status of toddlers from Puskesmas Ubung. The classification method proposed in this research is the KNN algorithm with Euclidean distance. There are three scenarios for the classification model that will be used. Performance classification will compare each model in terms of accuracy, precision, recall, f1-score, and mean absolute error.
Results: The experimental results show that KNN k = 15 using the first model generates excellent classification when classifying malnourished toddlers using the Euclidean distance function. The model obtains 91% accuracy, 86.6% precision, 83.8% recall, 85.2% recall, and a mean absolute error of 0.09.
Novelty: In this experiment, we analyzed the performance of the KNN to classify malnourished children using a nutritional status dataset, which resulted in an excellent classification that could be used for early detection.
Keywords
Full Text:
PDFReferences
C. Lowe et al., “The double burden of malnutrition and dietary patterns in rural Central Java, Indonesia,” Lancet Reg. Heal. - West. Pacific, vol. 14, 2021, doi: 10.1016/j.lanwpc.2021.100205.
R. N. Rachmawati and N. H. Pusponegoro, “Spatial Bayes Analysis on Cases of Malnutrition in East Nusa Tenggara, Indonesia,” Procedia Comput. Sci., vol. 179, pp. 337–343, 2021, doi: 10.1016/j.procs.2021.01.014.
Kemenkes RI, “Buletin Jendela Data dan Informasi Kesehatan: Situasi Balita Pendek di Indonesia,”Kementeri. Kesehat. RI, p. 20, 2018.
E. R. Arumi, Sumarno Adi Subrata, and Anisa Rahmawati, “Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 201–207, 2023, doi: 10.29207/resti.v7i2.4438.
W. Hanandita and G. Tampubolon, “The double burden of malnutrition in Indonesia: Social determinants and geographical variations,” SSM - Popul. Heal., vol. 1, pp. 16–25, 2015, doi: 10.1016/j.ssmph.2015.10.002.
K. L. Perdue et al., “Using functional near-infrared spectroscopy to assess social information processing in poor urban Bangladeshi infants and toddlers,” Dev. Sci., vol. 22, no. 5, 2019, doi: 10.1111/desc.12839.
V. Prasad, T. S. Rao, and M. S. P. Babu, “Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms,” Soft Comput., vol. 20, no. 3, pp. 1179–1189, 2016, doi: 10.1007/s00500-014-1581-5.
P. Theerthagiri, I. Jeena Jacob, A. Usha Ruby, and V. Yendapalli, “Prediction of covid-19 possibilities using knearest neighbour classification algorithm,” Int. J. Curr. Res. Rev., vol. 13, no. 6 special Issue, p. S-156-S-164, 2021, doi: 10.31782/IJCRR.2021.SP173.
R. Saxena, A. Johri, V. Deep, and P. Sharma, “Heart diseases prediction system using CHC-TSS evolutionary, KNN, and decision tree classification algorithm,” Adv. Intell. Syst. Comput., vol. 813, pp. 809–819, 2019, doi: 10.1007/978-981-13-1498-8_71.
K. A. Patil, K. V. M. Prashanth, and A. Ramalingaiah, “Texture feature extraction of Lumbar spine trabecular bone radiograph image using Laplacian of Gaussian filter with KNN classification to diagnose osteoporosis,” J. Phys. Conf. Ser., vol. 2070, no. 1, 2021, doi: 10.1088/1742-6596/2070/1/012137.
Z. Fan, J. K. Xie, Z. Y. Wang, P. C. Liu, S. J. Qu, and L. Huo, “Image Classification Method Based on Improved KNN Algorithm,” J. Phys. Conf. Ser., vol. 1930, no. 1, 2021, doi: 10.1088/1742-6596/1930/1/012009.
N. K. A. Wirdiani, P. Hridayami, N. P. A. Widiari, K. D. Rismawan, P. B. Candradinata, and I. P. D. Jayantha, “Face Identification Based on K-Nearest Neighbor,” Sci. J. Informatics, vol. 6, no. 2, pp. 150–159, Nov. 2019, doi: 10.15294/sji.v6i2.19503.
Z. Chen, L. J. Zhou, X. Da Li, J. N. Zhang, and W. J. Huo, “The Lao text classification method based on KNN,” Procedia Comput. Sci., vol. 166, pp. 523–528, 2020, doi: 10.1016/j.procs.2020.02.053.
E. Laksono, A. Basuki, and F. Bachtiar, “Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 2, pp. 377–383, 2020, doi: 10.29207/resti.v4i2.1845.
A. Patil and K. Lad, “Chili Plant Leaf Disease Detection Using SVM and KNN Classification,”Adv. Intell. Syst. Comput., vol. 1187, pp. 223–231, 2021, doi: 10.1007/978-981-15-6014-9_26.
A. Ali, M. Alrubei, L. F. M. Hassan, M. Al-Ja’afari, and S. Abdulwahed, “Diabetes classification based on KNN,” IIUM Eng. J., vol. 21, no. 1, pp. 175–181, 2020, doi: 10.31436/iiumej.v21i1.1206.
O. Altay and M. Ulas, “Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children,” 6th Int. Symp. Digit. Forensic Secur. ISDFS 2018 - Proceeding, vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/ISDFS.2018.8355354.
S. Reddi and G. V. Eswar, “Fake news in social media recognition using Modified Long ShortTerm Memory network,” Secur. IoT Soc. Networks, pp. 205–227, 2020, doi: 10.1016/B978-0-12-821599-9.00009-1.
A. Kulkarni, D. Chong, and F. A. Batarseh, “Foundations of data imbalance and solutions for a data democracy,” Data Democr. Nexus Artif. Intell. Softw. Dev. Knowl. Eng., pp. 83–106, 2020, doi: 10.1016/B978-0-12-818366-3.00005-8.
O. J. Awujoola, F. N. Ogwueleka, P. O. Odion, A. E. Awujoola, and O. R. Adelegan, “Genomic data science systems of Prediction and prevention of pneumonia from chest X-ray images using a two-channel dual-stream convolutional neural network,” Data Sci. Genomics, pp. 217–228, 2023, doi: 10.1016/b978-0-323-98352-5.00013-6.
D. Valero-carreras, J. Alcaraz, and M. Landete, “Computers and Operations Research Comparing two SVM models through different metrics based on the confusion matrix,” Comput. Oper. Res., vol. 152, no. April 2022, p. 106131, 2023, doi: 10.1016/j.cor.2022.106131.
O. F.Y, A. J.E.T, A. O, H. J. O, O. O, and A. J, “Supervised Machine Learning Algorithms: Classification and Comparison,” Int. J. Comput. Trends Technol., vol. 48, no. 3, pp. 128–138, Jun. 2017, doi: 10.14445/22312803/IJCTT-V48P126.
A. S. Rajawat, O. Mohammed, R. N. Shaw, and A. Ghosh, “Renewable energy system for industrial internet of things model using fusion-AI,” in Applications of AI and IOT in Renewable Energy, Elsevier, 2022, pp. 107–128. doi: 10.1016/B978-0-323-91699-8.00006-1.
S. Sendari, T. Widyaningtyas, and N. A. Maulidia, “Classification of Toddler Nutrition Status with Anthropometry using the K-Nearest Neighbor Method,” in 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Oct. 2019, pp. 1–5. doi: 10.1109/ICEEIE47180.2019.8981408.
R. Kaur, “Predicting diabetes by adopting classification approach in data mining,” Int. J. Informatics Vis., vol. 3, no. 2–2, pp. 218–221, 2019, doi: 10.30630/joiv.3.2-2.229.
A. Yudhana, A. Muslim, D. E. Wati, I. Puspitasari, A. Azhari, and M. M. Mardhia, “Human emotion recognition based on EEG signal using fast fourier transform and K-Nearest neighbor,”Adv. Sci. Technol. Eng. Syst., vol. 5, no. 6, pp. 1082–1088, 2020, doi: 10.25046/aj0506131.
Junadhi, Agustin, M. Rifqi, and M. K. Anam, “Sentiment Analysis of Online Lectures using KNearest Neighbors based on Feature Selection,” J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 216–225, 2022, doi: 10.23887/janapati.v11i3.51531.
Asundi Anand, “MATLAB for Photomechanics A Primer,” p. 199, 2002.
S. Ray, “A Quick Review of Machine Learning Algorithms,” in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, Feb. 2019, pp. 35–39. doi: 10.1109/COMITCon.2019.8862451
H. Abbad Ur Rehman, C. Y. Lin, and Z. Mushtaq, “Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease,” J. Chinese Inst. Eng. Trans. Chinese Inst. Eng. A, vol. 44, no. 1, pp. 77–87, 2021, doi: 10.1080/02533839.2020.1831967.
Z. Mushtaq, A. Yaqub, S. Sani, and A. Khalid, “Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets,” J. Chinese Inst. Eng. Trans. Chinese Inst. Eng. A, vol. 43, no. 1, pp. 80–92, 2020, doi: 10.1080/02533839.2019.1676658.
J. Maillo, S. Ramírez, I. Triguero, and F. Herrera, “kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data,” Knowledge-Based Syst., vol. 117, pp. 3–15, Feb. 2017, doi: 10.1016/j.knosys.2016.06.012.
B. V. V. S. Prasad, S. Gupta, N. Borah, R. Dineshkumar, H. K. Lautre, and B. Mouleswararao, “Predicting diabetes with multivariate analysis an innovative KNN-based classifier approach,”Prev. Med. (Baltim)., vol. 174, 2023, doi: 10.1016/j.ypmed.2023.107619.
A. Ali et al., “Machine learning approach for the classification of corn seed using hybrid features,”Int. J. Food Prop., vol. 23, no. 1, pp. 1097–1111, 2020, doi: 10.1080/10942912.2020.1778724.
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations,” pp. 1–10.
D. M. Atallah, M. Badawy, A. El-Sayed, and M. A. Ghoneim, “Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier,” Multimed. Tools Appl., vol. 78, no. 14, pp. 20383–20407, 2019, doi: 10.1007/s11042-019-7370-5.
B. Sahu, “Hybrid Approach for Breast Cancer Classification and Diagnosis,” EAI Endrosed Trans. Scalable Inf. Syst., doi: 10.4108/eai.19-12- 2018.156086.
P. Singh, N. Singh, K. K. Singh, and A. Singh, “Diagnosing of disease using machine learning,”Mach. Learn. Internet Med. Things Healthc., pp. 89–111, 2021, doi: 10.1016/B978-0-12-821229-5.00003-3.
R. S. Moorthy and P. Pabitha, “Optimal Detection of Phising Attack using SCA based K-NN,”Procedia Comput. Sci., vol. 171, no. 2019, pp. 1716–1725, 2020, doi: 10.1016/j.procs.2020.04.184.
W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. A. Abo-Elsoud, “A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier,”Knowledge-Based Syst., vol. 205, 2020, doi: 10.1016/j.knosys.2020.106270.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.