Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm


Maulana Muhamammad Jogo Samodro(1*), Muhammad Kunta Biddinika(2), Abdul Fadlil(3)

(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author


In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively.


Diabetes, Multilayer Perceptron, Machine Learning

Full Text:



M. Diana, E. Boyko, and I. Diabetes, International Diabetes Federation, vol. 10, no. 2. 2021.

K. Kannadasan, D. R. Edla, and V. Kuppili, “Type 2 diabetes data classification using stacked autoencoders in deep neural networks,” Clin. Epidemiol. Glob. Heal., vol. 7, no. 4, pp. 530–535, 2019, doi: https://doi.org/10.1016/j.cegh.2018.12.004.

S. A. Paschou, G. I. Sydney, K. J. Ioakim, K. Kotsa, and D. G. Goulis, “Comment on the systematic review and meta-analysis titled ‘Gestational diabetes and the risk of cardiovascular disease in women,’” Hormones, vol. 19, no. 3, pp. 447–448, 2020, doi: https://doi.org/10.1007/s42000-019-00158-w.

A. Pradhan, S. Prabhu, K. Chadaga, S. Sengupta, and G. Nath, “Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters,” Inf., vol. 13, no. 7, pp. 1–28, 2022, doi: 10.3390/info13070330.

R. Cheheltani dkk., “Predicting misdiagnosed adult-onset type 1 diabetes using machine learning,” Diabetes Res. Clin. Pract., vol. 191, p. 110029, 2022, doi: 10.1016/j.diabres.2022.110029.

M. Batta, “Machine Learning Algorithms - A Review,” Int. J. Sci. Res. (IJ, vol. 9, no. 1, pp. 381–386, 2020, doi: 10.21275/ART20203995.

A. Iyer, J. S, and R. Sumbaly, “Diagnosis of Diabetes Using Classification Mining Techniques,” Int. J. Data Min. Knowl. Manag. Process, vol. 5, no. 1, pp. 01–14, 2015, doi: 10.5121/ijdkp.2015.5101.

N. P. Tigga and S. Garg, “Prediction of Type 2 Diabetes using Machine Learning Classification Methods,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 706–716, 2020, doi: 10.1016/j.procs.2020.03.336.

A. V. Srinivas, A. Ramya, G. T. Chandralekha, B. Vaagdevi, and K. Anand Goud, “Prediction of Diabetes Using Machine Learning,” Ymer, vol. 21, no. 5, pp. 485–492, 2022, doi: 10.37896/YMER21.05/54.

M. A. Karan Manchandia*, Navdeep Khare, “Weka As a Data Mining Tool To Analyze Students’ Academic PERFORMANCES USING NAÏVE BAYES CLASSIFIER- A SURVEY,” Int. J. Eng. Sci. Res. Technol., vol. 6, no. 3, pp. 431–434, 2017, doi: 10.5281/zenodo.438104.

I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research,” Comput. Struct. Biotechnol. J., vol. 15, pp. 104–116, 2017, doi: 10.1016/j.csbj.2016.12.005.

E. G. Kulkarni and R. B. Kulkarni, “WEKA Powerful Tool in Data Mining General Terms,” International Journal of Computer Applications, vol. 5, no. Rtdm, pp. 975–8887, 2016.

I. N. A. Suprana, “Pengaruh Perbedaan Jumlah Hidden Layer dan Node pada Hidden Layer terhadap Performa Model Klasifikasi Diabetes,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 2, no. 2, pp. 411–419, 2022, doi: 10.24002/konstelasi.v2i2.5330.

M. K. Hasan, M. A. Alam, D. Das, E. Hossain, and M. Hasan, “Diabetes prediction using ensembling of different machine learning classifiers,” IEEE Access, vol. 8, pp. 76516–76531, 2020, doi: 10.1109/ACCESS.2020.2989857.

A. S. Musliman, A. Fadlil, and A. Yudhana, “Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction,” J. Online Inform., vol. 6, no. 1, pp. 63–72, 2021, doi: 10.15575/join.v6i1.704.

M. Fatima and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic,” J. Intell. Learn. Syst. Appl., vol. 09, no. 01, pp. 1–16, 2017, doi: 10.4236/jilsa.2017.91001.

Y. S. Park and S. Lek, “Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling,” in Developments in Environmental Modelling, vol. 28, Elsevier, 2016, pp. 123–140.

Z. Car, S. B. Šegota, N. Anđelić, I. Lorencin, and V. Mrzljak, “Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron,” Comput. Math. Methods Med., vol. 2020, p. 5714714, 2020, doi: 10.1155/2020/5714714.

I. Lorencin, N. Andelić, V. Mrzljak, and Z. Car, “Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation,” Energies, vol. 12, p. 4352, 2019, doi: 10.3390/en12224352.

Y. Ghanou and G. Bencheikh, “Architecture optimization and training for the multilayer perceptron using ant system,” IAENG Int. J. Comput. Sci., vol. 43, no. 1, pp. 20–26, 2016.

A. Music and S. Gagula-Palalic, “Classification of Leaf Type Using Multilayer Perceptron, Naive Bayes and Support Vector Machine Classifiers,” Southeast Eur. J. Soft Comput., vol. 5, no. 2, pp. 16–20, 2016, doi: 10.21533/scjournal.v5i2.119.

M. Nielsen, “How the backpropagation algorithm works,” 2019. http://neuralnetworksanddeeplearning.com/chap2.html (accessed Nov. 11, 2023).

A. H. Meftah, Y. A. Alotaibi, and S.-A. Selouani, “Evaluation of an Arabic Speech Corpus of Emotions: A Perceptual and Statistical Analysis,” IEEE Access, vol. 6, pp. 72845–72861, 2018, doi: 10.1109/ACCESS.2018.2881096.

V. R. Balaji, S. T. Suganthi, R. Rajadevi, V. Krishna Kumar, B. Saravana Balaji, and S. Pandiyan, “Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier,” Meas. J. Int. Meas. Confed., vol. 163, pp. 1–14, 2020, doi: 10.1016/j.measurement.2020.107922.

V. Doma and M. Pirouz, “A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00289-7.

D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 1578–1585, 2018, doi: 10.1016/j.procs.2018.05.122.

DOI: https://doi.org/10.22146/ijccs.94575

Article Metrics

Abstract views : 825 | views : 511


  • There are currently no refbacks.

Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs

View My Stats1
View My Stats2