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Image of PREDIKSI POLA HENTI JANTUNG BERDASARKAN DELAPAN DATA TANDA VITAL MENGGUNAKAN METODE RECURRENT NEURAL NETWORK

Skripsi

PREDIKSI POLA HENTI JANTUNG BERDASARKAN DELAPAN DATA TANDA VITAL MENGGUNAKAN METODE RECURRENT NEURAL NETWORK

Rafika, Salwa Ayu - Personal Name;

Penilaian

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Penilaian anda saat ini :  

The ability to predict patterns of Cardiac Arrest with high accuracy can significantly contribute to more effective prevention and treatment. This research focuses on predicting patterns of Cardiac Arrest based on eight vital sign data using Recurrent Neural Network (RNN) methods with LSTM and Bi-LSTM architectures. In order to address the issue of imbalanced data, undersampling techniques and class weighting with cost-sensitive learning approach are employed. To fill in missing values in the dataset, this study utilizes Linear Interpolation as well as Deep Learning techniques such as Autoencoder and U-Net for data imputation. The best performance is achieved by the cost-sensitive Bi-LSTM (CSBi-LSTM) model without undersampling the majority class. Linear Interpolation is applied for data imputation with a total data duration and prediction range of 60 minutes. The evaluation results of the CSBi-LSTM model on accuracy, sensitivity, precision, f1-score, and specificity metrics are 95%, 95%, 5%, 10%, and 100% respectively.


Availability
Inventory Code Barcode Call Number Location Status
2307002410T113569T1135692023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1135692023
Publisher
Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023
Collation
xiv, 71 hlm.; Ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.307
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Sistem Komputer
Processing Mode
Specific Detail Info
-
Statement of Responsibility
FIRA
Other version/related

No other version available

File Attachment
  • PREDIKSI POLA HENTI JANTUNG BERDASARKAN DELAPAN DATA TANDA VITAL MENGGUNAKAN METODE RECURRENT NEURAL NETWORK
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