An XGBoost Model for Age Prediction from COVID-19 Blood Test

Nunung Nurul Qomariyah; Ardimas Andi Purwita; M.S. Astriani; Sri Dhuny Atas Asri; Dimitar Lubomirov Kazakov

An XGBoost Model for Age Prediction from COVID-19 Blood Test

Nunung Nurul Qomariyah, Ardimas Andi Purwita, M.S. Astriani, Sri Dhuny Atas Asri, Dimitar Lubomirov Kazakov

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

COVID-19 was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients’ routine blood test results and their age. Predicting a person’s age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients’ age from their routine blood tests. The performance evaluation is very satisfactory, with R2 > 0.80 and a normalized RMSE below 0.1.

Original language	English
Title of host publication	Proceedings of the 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)
Publisher	IEEE
Number of pages	7
ISBN (Electronic)	9781665401500
Publication status	Published - 16 Dec 2021
Event	INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS - Virtual conference, YOGYAKARTA, Indonesia Duration: 16 Dec 2021 → 17 Dec 2021 Conference number: 4 https://isriti.akakom.ac.id/index.html

Conference

Conference	INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS
Abbreviated title	ISRITI
Country/Territory	Indonesia
City	YOGYAKARTA
Period	16/12/21 → 17/12/21
Internet address	https://isriti.akakom.ac.id/index.html

Bibliographical note

© IEEE, 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Access to Document

COVID_Age_Based_Group_Analysis
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Accepted author manuscript, 1.39 MBLicence: Other

Cite this

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title = "An XGBoost Model for Age Prediction from COVID-19 Blood Test",

abstract = "COVID-19 was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients{\textquoteright} routine blood test results and their age. Predicting a person{\textquoteright}s age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients{\textquoteright} age from their routine blood tests. The performance evaluation is very satisfactory, with R2 > 0.80 and a normalized RMSE below 0.1.",

author = "Qomariyah, {Nunung Nurul} and Purwita, {Ardimas Andi} and M.S. Astriani and Asri, {Sri Dhuny Atas} and Kazakov, {Dimitar Lubomirov}",

note = "{\textcopyright} IEEE, 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details ; INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS, ISRITI ; Conference date: 16-12-2021 Through 17-12-2021",

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Qomariyah, NN, Purwita, AA, Astriani, MS, Asri, SDA & Kazakov, DL 2021, An XGBoost Model for Age Prediction from COVID-19 Blood Test. in Proceedings of the 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS, YOGYAKARTA, Indonesia, 16/12/21.

TY - GEN

T1 - An XGBoost Model for Age Prediction from COVID-19 Blood Test

AU - Qomariyah, Nunung Nurul

AU - Purwita, Ardimas Andi

AU - Astriani, M.S.

AU - Asri, Sri Dhuny Atas

AU - Kazakov, Dimitar Lubomirov

N1 - Conference code: 4

PY - 2021/12/16

Y1 - 2021/12/16

N2 - COVID-19 was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients’ routine blood test results and their age. Predicting a person’s age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients’ age from their routine blood tests. The performance evaluation is very satisfactory, with R2 > 0.80 and a normalized RMSE below 0.1.

AB - COVID-19 was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients’ routine blood test results and their age. Predicting a person’s age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients’ age from their routine blood tests. The performance evaluation is very satisfactory, with R2 > 0.80 and a normalized RMSE below 0.1.

M3 - Conference contribution

BT - Proceedings of the 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

PB - IEEE

T2 - INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS

Y2 - 16 December 2021 through 17 December 2021

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