DOI: https://doi.org/10.21456/vol14iss3pp256-266

Prediksi Perubahan Hemodinamik Pasien setelah Pemberian Premedikasi menggunakan Machine Learning Neural Network Guna Meningkatkan Kinerja Penanganan Medis

*Jiyestha Aji Dharma Aryasa  -  Master of Information System, Postgraduate Program, Universitas Diponegoro, Jl. Imam Bardjo SH No.5, Pleburan, Semarang, Indonesia 50241, Indonesia
Aris Puji Widodo  -  Master of Information System, Postgraduate Program, Universitas Diponegoro, Jl. Imam Bardjo SH No.5, Pleburan, Semarang, Indonesia 50241, Indonesia
Catur Edi Widodo  -  Master of Information System, Postgraduate Program, Universitas Diponegoro, Jl. Imam Bardjo SH No.5, Pleburan, Semarang, Indonesia 50241, Indonesia
Open Access Copyright (c) 2024 Jurnal Sistem Informasi Bisnis

Citation Format:
Abstract
This research presents the development process of a machine learning neural network model for predicting hemodynamic changes in patients after premedication, aiming to enhance the performance of medical interventions. The model was constructed using 3055 patients’ data who underwent premedication processes. The developed neural network model has an architecture consisting of 10 nodes in the input layer, 10 nodes in the hidden layer, and 3 nodes in the output layer. The evaluation results of the model indicate an overall accuracy of 85%. The precision values are high for normal class predictions at 0.85 and for hypertension class predictions at 0.81 with corresponding recalls of 1 (high) and 0.6 (moderate), respectively. However, predictions for the hypotension class still have a low precision of 0.6 and a recall of 0.04 (very low) due to the significantly lower number of samples in the hypotension class compared to the normal and hypertension classes. While testing with new data, the model has successfully predicted whether patients will experience hemodynamic pressure changes. It is expected that this model can contribute to improving the performance of medical interventions, thereby minimizing undesirable hemodynamic pressure changes.
Fulltext View|Download
Keywords: Pembelajaran Mesin; Jaringan Saraf Tiruan; Feedforward; Hemodinamik; Premedikasi

Article Metrics:

Article Info
Section: Research Articles
Language : ID
Recent articles
Pengembangan Game Edukasi Digital Tema Seni dan Bahasa dengan Metode Multimedia Development Life Cycle Analisis Penerimaan dan Kesuksesan Aplikasi M-health pada Lansia menggunakan Model UTAUT dan Delone & McLean Perbandingan Metode Machine Learning dalam Analisis Sentimen Komentar Pengguna Aplikasi InDriver pada Dataset Tidak Seimbang Back matter JSINBIS 14 (3) 2024 Front matter JSINBIS 14 (3) 2024 Preface JSINBIS 14 (3) 2024 More recent articles
Most cited articles
Metode Quality Function Deployment Dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Pemilihan Perusahaan Penyedia Jasa Internet Aplikasi Penentuan Tarif Listrik Menggunakan Metode Fuzzy Sugeno Sistem Gesture Accelerometer dengan Metode Fast Dynamic Time Warping (FastDTW) Sistem Informasi Pengukuran Kinerja Pada Perkebunan Kelapa Sawit Dengan Menggunakan Metode Balanced Scorecard Review: Implementasi Holap Untuk Optimasi Query Sistem Basis Data Terdistribusi Dengan Pendekatan Algoritma Genetik More cited articles
  1. Letaifa, A.B., 2019. Chapter Four - SSIM and ML Based QoE Enhancement Approach in SDN Context. Advances in Computers, 114, 151-196. https://doi.org/10.1016/bs.adcom.2019.02.004
  2. Currie, G., Hawk, K.E., Rohren, E., Vial, A., Klein, R., 2019. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. Journal of Medical Imaging and Radiation Sciences, 50(4), 477-487. https://doi.org/10.1016/j.jmir.2019.09.005
  3. Dastres, R., Soori, M., 2021. Artificial Neural Network Systems. International Journal of Imaging and Robotics (IJIR), 21(2), 13-25
  4. Dung-Hung, C., Cong, T., Zeyu, J., Yu-Shan, O.-Y., Yung-Yan, L., 2022. External Validation of a Machine Learning Model to Predict Hemodynamic Instability in Intensive Care Unit. Critical Care, 26(215), 1-10. https://doi.org/10.1186/s13054-022-04088-9
  5. El Bouchefry, K., de Souza, R.S., 2020. Chapter 12 - Learning in Big Data: Introduction to Machine Learning. Knowledge Discovery in Big Data from Astronomy and Earth Observation, 225-249. https://doi.org/10.1016/B978-0-12-819154-5.00023-0
  6. Janiesch, C., Zschech, P., Heinrich, K., 2021. Machine Learning and Deep Learning. Electronic Markets, 31, 685-695. https://doi.org/10.1007/s12525-021-00475-2
  7. Jeong, Y.-S., Kang, A.R., Jung, W., Lee, S.J., Lee, S., Lee, M., Chung, Y.H., Koo, B.S., Kim, S.H., 2019. Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study. Applied Sciences, 9(23), 5135. https://doi.org/10.3390/app9235135
  8. Koshire, A., Kandikatla, P.S., Pawar, H., 2022. Comparison of Haemodynamic Response among Patients Posted for Laparoscopic Cholecystectomy with or without Oral Clonidine as Premedication- A Prospective Comparative Study. MVP Journal of Medical Sciences, 8(1), 54-59
  9. Simonneau, G., Montani, D., Celermajer, D.S., Denton, C.P., Gatzoulis, M.A., Krowka, M., Williams, P.G., Souza, R., 2019. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. European Respiratory Journal, 53(1), 1801913. https://doi.org/10.1183/13993003.01913-2018
  10. Urso, A., Fiannaca, A., La Rosa, M., Ravì, V., Rizzo, R., 2019. Data Mining: Prediction Methods. Encyclopedia of Bioinformatics and Computational Biology. 1, 413–430. https://doi.org/10.1016/B978-0-12-809633-8.20462-7
  11. Xie, Y., Chen, Y., Lian, Q., Yin, H., Peng, J., Sheng, M., Wang, Y., 2022. Enhancing Real-Time Prediction of Effluent Water Quality of Wastewater Treatment Plant Based on Improved Feedforward Neural Network Coupled with Optimization Algorithm. Water, 14(7), 1053. https://doi.org/10.3390/w14071053
  12. Yakimovich, A., Beaugnon, A., Huang, Y., Ozkirimli, E., 2021. Labels in a Haystack: Approaches Beyond Supervised Learning in Biomedical Applications. Patterns, 2(12), 1-11. https://doi.org/10.1016/j.patter.2021.100383

Last update:

No citation recorded.

Last update: 2024-11-20 16:56:48

No citation recorded.