Model Prediction Using Artificial Neural Network (ANN) to Strengthen Diagnostic Analysis of Diabetes Melitus
DOI:
https://doi.org/10.21512/comtech.v15i2.11905Keywords:
model prediction, Artificial Neural Network (ANN), diagnostic analysis, diabetes mellitusAbstract
The incidence of Diabetes Mellitus (DM) cases is one of the urgent and increasing health issues every year. Hence, this condition requires high urgency to be handled. The research aimed to develop a prediction model for DM that could be used in general for the purpose of diagnostic analysis of DM cases against suspected individuals. The dataset was sourced from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), which had closely related parameters in diagnostic analysis efforts without favoring certain groups. The targeted contribution was the result of a new prediction model that was specifically tested on the dataset using the Artificial Neural Network (ANN) algorithm. This model was developed through a baseline model that was tested and improved in performance through hyperparameter cross-validation therapy and L1 regularization. The formation of the model architecture through experiments to adjust the conditions of hidden layers and neurons in several configurations results in a model architecture with 8 input parameters. It contains 3 hidden layers with a total of 14, 20, and 26 neurons, with the ReLU activation function on each hidden layer and the Sigmoid activation function on the output part. The second test is carried out on a hyperparameter configuration. It produces maximum performance with a k-fold value of 10 and L1 regularization of 0.0001. The model performance results obtain an accuracy value of 0.947, precision of 0.895, recall of 0.914, and model loss of 0.215
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References
Al Sadi, K., & Balachandran, W. (2023). Prediction model of type 2 diabetes mellitus for Oman prediabetes patients using artificial neural network and six machine learning classifiers. Applied Sciences, 13(4), 1–22. https://doi.org/10.3390/app13042344
Alifiah, N., Kurniasari, D., Amanto, & Warsono. (2023). Prediction of COVID-19 using the Artificial Neural Network (ANN) with k-fold cross-validation. Journal of Information Systems Engineering and Business Intelligence, 9(1), 16–27. https://doi.org/10.20473/jisebi.9.1.16-27
Anil, A. K. P., & Singh, U. K. (2023). An optimal solution to the overfitting and underfitting problem of healthcare machine learning models. Journal of Systems Engineering and Information Technology (JOSEIT), 2(2), 77–84. https://doi.org/10.29207/joseit.v2i2.5460
Benmansour, H. (2021, June). Predict diabetes based on diagnostic measures. Kaggle. https://www.kaggle.com/datasets/houcembenmansour/predict-diabetes-based-on-diagnostic-measures
Chandra, J. B., & Nasien, D. (2023). Application of machine learning k-nearest neighbour algorithm to predict diabetes. International Journal of Electrical, Energy and Power System Engineering (IJEEPSE), 6(2), 134–139.
Dewi, D. D., Qisthi, N., Lestari, S. S. S., & Putri, Z. H. S. (2023). Perbandingan metode neural network dan support vector machine dalam klasifikasi diagnosa penyakit diabetes. Cerdika: Jurnal Ilmiah Indonesia, 3(9), 828–839. https://doi.org/10.59141/cerdika.v3i09.662
Feng, X., Cai, Y., & Xin, R. (2023). Optimizing diabetes classification with a machine learning-based framework. BMC Bioinformatics, 24, 1–20. https://doi.org/10.1186/s12859-023-05467-x
Halim, S. F. N., & Azmi, U. (2023). Analisis perbandingan klasifikasi dan penerapan teknik SMOTE dalam imbalanced data pada credit card default. Jurnal Sains dan Seni, 12(2), D127–D134. https://doi.org/10.12962/j23373520.v12i2.111833
Handayani, M., Riandini, M., & Situmorang, Z. (2022). Perbandingan fungsi optimasi neural network dalam klasifikasi kelayakan calon suami. Jurnal Informatika, 9(1), 78–84. https://doi.org/10.31294/inf.v9i1.12318
Izzo, A., Massimino, E., Riccardi, G., & Della Pepa, G. (2021). A narrative review on sarcopenia in type 2 diabetes mellitus: Prevalence and associated factors. Nutrients, 13(1), 1–18. https://doi.org/10.3390/nu13010183
Kadhim, Z. S., Abdullah, H. S., & Ghathwan, K. I. (2023). Automatically avoiding overfitting in deep neural networks by using hyper-parameters optimization methods. International Journal of Online and Biomedical Engineering (IJOE), 19(05), 146–162. https://doi.org/10.3991/ijoe.v19i05.38153
Li, M. (2023). A practical significant technic in solving overfitting: Regularization. In Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023) (pp. 253–258). https://doi.org/10.54254/2753-8818/5/20230433
Maulana, M. R., Abdunnur, & Syahrir, M. R. (2022). Analisis Kuartil, Desil Dan Persentil Pada Ukuran Panjang Udang Loreng (Mierspenaeopsis sculptilis) di perairan Muara Ilu Kabupaten Kutai Kartanegara. Jurnal Tropical Aquatic Sciences, 1(1), 10–16. https://doi.org/10.30872/tas.v1i1.467
Michelucci, U. (2018). Applied deep learning: A case-based approach to understanding deep neural networks. Apress. https://doi.org/10.1007/978-1-4842-3790-8
Mustafa, H., Mohamed, C., Nabil, O., & Noura, A. (2023). Machine learning techniques for diabetes classification: A comparative study. International Journal of Advanced Computer Science and Applications (IJACSA), 14(9), 785–790. https://doi.org/10.14569/IJACSA.2023.0140982
Mustofa, F., Safriandono, A. N., Muslikh, A. R., & Setiadi, D. R. I. M. (2023). Dataset and feature analysis for diabetes mellitus classification using random forest. Journal of Computing Theories and Applications, 1(1), 41–49. https://doi.org/10.33633/jcta.v1i1.9190
Naseem, A., Habib, R., Naz, T., Atif, M., Arif, M., & Allaoua Chelloug, S. (2022). Novel Internet of Things based approach toward diabetes prediction using deep learning models. Frontiers in Public Health, 10, 1–11. https://doi.org/10.3389/fpubh.2022.914106
Nguyen, T. A., Ly, H. B., & Tran, V. Q. (2021). Investigation of ANN architecture for predicting load‐carrying capacity of castellated steel beams. Complexity, 2021(1), 1–14. https://doi.org/10.1155/2021/6697923
Nurul Anisa, D., & Jumanto. (2022). Klasifikasi penyakit diabetes menggunakan algoritma naive bayes. Dinamika Informatika, 14(1), 33–42.
Piko, P., Werissa, N. A., Fiatal, S., Sandor, J., & Adany, R. (2021). Impact of genetic factors on the age of onset for type 2 diabetes mellitus in addition to the conventional risk factors. Journal of Personalized Medicine, 11(1), 1–17. https://doi.org/10.3390/jpm11010006
Prasetyo, A. B., & Laksana, T. G. (2022). Optimasi algoritma K-Nearest Neighbors dengan teknik cross validation dengan streamlit (Studi data: Penyakit diabetes). Journal of Applied Informatics and Computing (JAIC), 6(2), 194.
Prasetyo, S. Y. (2023). Prediksi gagal jantung menggunakan artificial neural network. Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen, 13(1), 79–88. https://doi.org/10.33020/saintekom.v13i1.379
Richens, J. G., Lee, C. M., & Johri, S. (2020). Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications, 11, 1–9. https://doi.org/10.1038/s41467-020-17419-7
Sharma, T., & Shah, M. (2021). A comprehensive review of machine learning techniques on diabetes detection. Visual Computing for Industry, Biomedicine, and Art, 4, 1–16. https://doi.org/10.1186/s42492-021-00097-7
Sihombing, P. R., Suryadiningrat, Sunarjo, D. A., & Yuda, Y. P. A. C. (2023). Identifikasi data outlier (pencilan) dan kenormalan data pada data univariat serta alternatif penyelesaiannya. Jurnal Ekonomi dan Statistik Indonesia, 2(3), 307–316. https://doi.org/10.11594/jesi.02.03.07
Syukron, A., Sardiarinto, Saputro, E., & Widodo, P. (2023). Penerapan metode SMOTE untuk mengatasi ketidakseimbangan kelas pada prediksi gagal jantung. Jurnal Teknologi Informasi dan Terapan, 10(1), 47–50. https://doi.org/10.25047/jtit.v10i1.313
Thotad, P., Bharamagoudar, G. R., & Anami, B. S. (2022). Predictive analysis of diabetes mellitus using decision tree approach. In 2022 2nd Asian Conference on Innovation in Technology (ASIANCON) (pp. 1–7). https://doi.org/10.1109/ASIANCON55314.2022.9909122
Vijayalakshmi, V., & Venkatachalapathy, K. (2019). Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data. International Journal of Recent Technology and Engineering (IJRTE), 8(2), 5073–5081. https://doi.org/10.35940/ijrte.B2155.078219
Zaman, Z., Shohas, M. A. A. A., Bijoy, M. H., Hossain, M., & Al Sakib, S. (2022). Assessing machine learning methods for predicting diabetes among pregnant women. International Journal of Advancement in Life Sciences Research, 5(1), 29–34. https://doi.org/10.31632/ijalsr.2022.v05i01.005
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