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PREDIKSI MORTALITAS PADA PASIEN INSTALASI PERAWATAN INTENSIF MENGGUNAKAN DEEP LEARNING

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Mortality prediction for patients in intensive care units (ICUs) is crucial for care planning, resource allocation, and timely medical decision-making. To enhance the accuracy of these predictions, artificial intelligence (AI) technology has shown promising results. This study aims to develop deep learning methods to improve the accuracy of mortality predictions for ICU patients, addressing missing values and class imbalance in multivariate time-series data. The research methodology includes literature review, data collection from the MIMIC-IV database, data preprocessing, model development, testing, validation, and result analysis. The preprocessing stage involves feature selection, data pivoting, data filtering, data encoding, data imputation, and data balancing. The deep learning methods applied include GRU, LSTM, RNN, CNN, Bi-LSTM, Stacked LSTM, and 1D-MSNet. Data imputation is performed using linear interpolation and XU-Net, while undersampling and SMOTE techniques are used to address class imbalance. The study results show that the 1D-MSNet method achieves 99% accuracy on training data and 96% on validation and testing data, with an AUC value approaching 100%, indicating very high predictive capability. In conclusion, deep learning methods, particularly 1D-MSNet, are effective in improving the accuracy of mortality predictions for ICU patients. The use of XU-Net is effective in handling missing values, and SMOTE techniques are effective in addressing class imbalance, significantly enhancing model performance. Keywords : Mortality prediction, deep learning, MIMIC-IV

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Detail Information

Series Title

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Call Number

T1562922024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 82 hlm.; ilus,; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Kecerdasan Buatan
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEW

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