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MULTIVARIATE IMPUTATION TANDA VITAL PASIEN UNIT PERAWATAN INTENSIF MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. 

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Missing data is a common problem in vital sign datasets and is one of the most complex topics in computer science. The large number of missing values in the data makes it more difficult to process. Therefore, one way to address missing data is through imputation. Vital sign data imputation is the process of filling in missing or incomplete data in a patient's medical records. Machine learning for data imputation has been frequently applied, but tends to perform poorly with datasets that have high levels of missing values. Hence, deep learning methods are used, as they have the ability to unearth and capture hidden information in data that leads to progress in data imputation. One of the methods used for vital sign data imputation is the Autoencoder and Convolutional Neural Network (CNN). Recent studies have shown that CNN and Autoencoder are two methods that are quite effective in performing vital sign data imputation. In this study, the performance between Autoencoder and CNN methods in imputing vital sign data in the medical information mart for intensive care III (MIMIC III) database will be compared. The CNN resulted in the smallest RMSE value of 0.0306 while the Autoencoder resulted in the smallest RMSE value of 0.0642. The results of vital sign data imputation using CNN showed superior performance compared to Autoencoder.

Availability
Inventory Code Barcode Call Number Location Status
2307003937T104475T1044752023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1044752023
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiii, 64 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
005.707
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Sistem Komputer
Data dalam sistem-sistem komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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