Machine-learning models for predicting survivability in COVID-19 patients

Acheme, Ijegwa David; Vincent, Olufunke Rebecca

doi:10.1016/b978-0-12-824536-1.00011-3

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Preprocessing melibatkan serangkaian langkah berulang yang bertujuan mengubah data mentah menjadi bentuk yang dapat dipahami dan dimanfaatkan lebih lanjut. Data mentah seringkali memiliki karakteristik seperti ketidaklengkapan, ketidakkonsistenan, kurangnya struktur, dan mungkin mengandung kesalahan [11]. Tujuannya adalah untuk mempersiapkan data dengan cermat agar lebih optimal digunakan dalam tahap berikutnya.…”

Section: A Data Preprocessingunclassified

“…Pemilihan fitur yang efektif memastikan bahwa model menjadi lebih sederhana, memudahkan interpretasi oleh peneliti dan pengguna. Selain itu, dengan mengurangi jumlah variabel input yang digunakan dalam pengembangan model, proses ini dapat mengurangi biaya komputasi model dan meningkatkan kinerjanya [11]. Pemilihan fitur adalah proses memilih subset fitur yang relevan dan berpengaruh besar dari serangkaian fitur yang tersedia untuk dianalisis.…”

Section: ) Metode Pemilihan Fiturunclassified

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Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier

Joses,

Quinevera,

Mardianto

et al. 2024

JEPIN

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Serangan Distributed Denial of Service (DDoS) adalah jenis serangan yang kompleks dan sering melibatkan berbagai pola lalu lintas jaringan yang berbeda. Model soft voting classifier dapat menggabungkan hasil dari beberapa model klasifikasi yang berbeda, sehingga meningkatkan kemampuan untuk mendeteksi dan mengatasi serangan DDoS dengan berbagai pola dan skenario yang berbeda. Dengan memanfaatkan model soft voting classifier berdasarkan fitur-fitur yang mendukung, dapat meningkatkan ketahanan sistem terhadap serangan DDoS dengan lebih efektif, mengurangi dampaknya, dan memastikan ketersediaan sumber daya jaringan dan layanan internet bagi pengguna yang mengaksesnya. Data yang digunakan dalam penelitian ini menggunakan dataset DDoS yang diperoleh dari situs kaggle.com. Dataset ini memiliki 23 atribut termasuk satu variabel output dengan jumlah data sebanyak 104.245 record. Dilakukan preprocessing pada dataset kemudian diklasifikasi menggunakan lima model machine learning dan sepuluh ensemble learning method untuk mendapatkan hasil akurasi tertinggi. Hasil pengujian menunjukkan bahwa ensemble method sangat optimal dalam mendeteksi serangan DDoS baik menggunakan fitur berdasarkan Information Gain maupun menggunakan fitur berdasarkan Gain Ratio dibandingkan dengan metode machine learning tunggal.

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Section: A Data Preprocessingunclassified

Section: ) Metode Pemilihan Fiturunclassified

Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier

Joses,

Quinevera,

Mardianto

et al. 2024

JEPIN

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“…Another research used developed SVR and random forest (RF) ensemble predictors to predict patient numbers before being hospitalized [19], [20]. The deep learning algorithm demonstrated a vital role in studying and predicting enormous outbreak data patterns and helped prevent coronavirus's high spread in early exploitation [21]. Based on the research that has been carried out by different researchers, in this study, long short-term memory network (LSTM) is being proposed as an analyzing tool for COVID-19 due to its time-series nature.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

Modeling recurrence of COVID-19 and its variants using recurrent neural network

Bolarinwa

Vincent²,

Aborisade³

et al. 2023

Bulletin EEI

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Coronavirus disease 19 (COVID-19), a disease caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), began as the flu and gradually developed into a highly infectious global pandemic leading to the death of over 6 million people in about 200 countries of the world. Its pathogenic nature has qualified it as a deadly disease, causing moderate and severe respiratory difficulty in infected individuals with the ability to mutate into different variants of the first version. As a result, different government agencies and health institutions have sought solutions within and outside the clinical space. This paper models COVID-19 possible recurrence as variants and predicts that the subsequent waves will be more severe than the first wave. Long short-term memory network (LSTM) was used to predict the future occurrence of COVID-19 and forecast the virus's pattern. Machine evaluation was performed using precision, recall, F1-score, an area under the curve (AUC), and accuracy evaluation metrics. Datasets obtained were used to test the data. The collected characteristics were passed on to the system classification network, demonstrating the function's value based on the system's accuracy. The results showed that the COVID-19 variants have a higher disastrous effect within three months after the first wave.

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“…The focus of this study is to predict the survivability of infected people who are at risk of dying from COVID-19. In the United States and the United Kingdom, the risk of death is higher than in Asia (Acheme & Vincent, 2021). The steps taken are ordering citizens to stay at home, avoiding crowds, washing hands regularly, maintaining a distance of 1-2 meters, and being prohibited from touching their faces.…”

Section: Introductionmentioning

confidence: 99%

Correlation of Factors Causing the Death of COVID-19 Patients and Enforcement of Regulations in Handling COVID-19 in the City of Bogor

Indrianingrum

Wiranta

2021

J. Bina Praja

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The outbreak of the COVID-19 virus has attacked all countries in the world and become a serious concern, also in Indonesia. Indonesia is trying to fight the spread of the COVID-19 disease with various forms of regulations issued, including the Government, Central Government, and District/City Governments. This policy was carried out to anticipate and prevent the spread of the COVID-19 virus outbreak. The study aims to look at the policies that the Bogor City Government has implemented and empirically test the data on COVID-19 patients to determine the factors that can influence the death of COVID-19 patients. The results showed that almost all COVID-19 patients adhere to the health protocol set by the Bogor City Government, but COVID-19 patients who are elderly, male, and have comorbid disease have a high risk of dying.

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Machine-learning models for predicting survivability in COVID-19 patients

Cited by 12 publications

References 25 publications

Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier

Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier

Modeling recurrence of COVID-19 and its variants using recurrent neural network

Correlation of Factors Causing the Death of COVID-19 Patients and Enforcement of Regulations in Handling COVID-19 in the City of Bogor

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