Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.754Keywords:
Dengue Fever; Typhus; Deep Learning; MLP; Python; ANNAbstract
Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
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References
B. C. Bohm et al., “Utilization of machine learning for dengue case screening,” BMC Public Health, vol. 24, no. 1, pp. 1–9, 2024.
M. T. Phillips et al., “A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data,” Stat. Med., vol. 40, no. 26, pp. 5853–5870, 2021.
S. A. alias Balamurugan, M. S. M. Mallick, and G. Chinthana, “Improved prediction of dengue outbreak using combinatorial feature selector and classifier based on entropy weighted score based optimal ranking,” Informatics Med. Unlocked, vol. 20, no. July, p. 100400, 2020.
G. Gupta et al., “DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms,” Diagnostics, vol. 13, no. 6, 2023.
M. Tunay, E. Pashaei, and E. Pashaei, “Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification,” Comput. Intell. Neurosci., vol. 2022, 2022.
L. Chen, X. Fan, K. Mao, A. Tolba, F. Alqahtani, and A. M. Ahmed, “Study of Multidimensional and High-Precision Height Model of Youth Based on Multilayer Perceptron,” Comput. Intell. Neurosci., vol. 2022, 2022.
J. Hwang et al., “Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students,” Sensors, vol. 24, no. 13, pp. 1–13, 2024.
A. Al Bataineh and S. Manacek, “MLP-PSO Hybrid Algorithm for Heart Disease Prediction,” J. Pers. Med., vol. 12, no. 8, 2022.
R. Ali, J. Hussain, and S. W. Lee, “Multilayer perceptron-based self-care early prediction of children with disabilities,” Digit. Heal., vol. 9, 2023.
Z. Zhang et al., “Multilayer perceptron-based prediction of stroke mimics in prehospital triage,” Sci. Rep., vol. 12, no. 1, pp. 1–8, 2022.
M. Lutz, Learning Python, vol. 78, no. 1. 2007.
R. Zhang, L. Wang, S. Cheng, and S. Song, “MLP-based classification of COVID-19 and skin diseases,” Expert Syst. Appl., vol. 228, no. March, p. 120389, 2023.
M. Wang, J. Zhang, X. Wang, B. Zhang, and Z. Yang, “Source Discrimination of Mine Water by Applying the Multilayer Perceptron Neural Network (MLP) Method—A Case Study in the Pingdingshan Coalfield,” Water (Switzerland), vol. 15, no. 19, 2023.
E. Joelianto et al., “Convolutional neural network-based real-time mosquito genus identification using wingbeat frequency: A binary and multiclass classification approach,” Ecol. Inform., vol. 80, no. January, p. 102495, 2024.
M. A. R. Khan, J. Akter, I. Ahammad, S. Ejaz, and T. Jaman Khan, “Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree,” Heal. Inf. Sci. Syst., vol. 10, no. 1, pp. 1–18, 2022.
E. S. Mohamed, T. A. Naqishbandi, S. A. C. Bukhari, I. Rauf, V. Sawrikar, and A. Hussain, “A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms,” Healthc. Anal., vol. 3, no. March, p. 100185, 2023.
Y. Joo et al., “Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms,” Sci. Rep., vol. 13, no. 1, pp. 1–15, 2023.
A. Orooji, M. Shanbehzadeh, E. Mirbagheri, and H. Kazemi-Arpanahi, “Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19,” BMC Infect. Dis., vol. 22, no. 1, pp. 1–13, 2022.
X. Hu et al., “Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis,” BMC Infect. Dis., vol. 22, no. 1, pp. 1–16, 2022.
A. El Hadiri, L. Bahatti, A. El Magri, and R. Lajouad, “A Novel Brain-Machine Safety System Based on Drowsiness Detection using the PNN and MLP Algorithms,” IFAC-PapersOnLine, vol. 58, no. 13, pp. 823–828, 2024.
S. Karolcik et al., “Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study,” eBioMedicine, vol. 104, p. 105164, 2024.
J. K. Chaw et al., “A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients,” Healthc. Anal., vol. 5, no. November 2023, p. 100290, 2024.
Z. Mumtaz, Z. Rashid, R. Saif, and M. Z. Yousaf, “Deep learning guided prediction modeling of dengue virus evolving serotype,” Heliyon, vol. 10, no. 11, p. e32061, 2024.
T. T. Han et al., “Machine learning based classification model for screening of infected patients using vital signs,” Informatics Med. Unlocked, vol. 24, p. 100592, 2021.
A. Elhadad et al., “Improved healthcare diagnosis accuracy through the application of deep learning techniques in medical transcription for disease identification,” Alexandria Eng. J., vol. 123, no. March, pp. 112–123, 2025.
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