The Classification of Patient Blood Samples with Machine Learning Techniques

Qian DENG; Jiayu LYU; Haowen ZOU

doi:10.1117/12.2673747

The Classification of Patient Blood Samples with Machine Learning Techniques

Qian DENG^*, Jiayu LYU, Haowen ZOU

^*Corresponding author for this work

School of Graduate Studies

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

After implementing the Jaminan Kesehatan Nasional (JKN) in Indonesia, health system inequity, payment non-compliance and additional expenditure still exists. To better deal with the problems in their healthcare system, this study uses a variety of machine learning algorithms to classify patient blood samples for improving the efficiency of healthcare system. The study shows that most of the algorithms are up to 70% accuracy and the accuracy will rise with only important variables.

Original language	English
Title of host publication	Proceedings of SPIE : The International Society for Optical Engineering
Editors	Hongzhi WANG, Xiangjie KONG
Publisher	SPIE
Volume	12640
ISBN (Electronic)	9781510664913
DOIs	https://doi.org/10.1117/12.2673747
Publication status	Published - 22 May 2023
Event	2022 International Conference on Internet of Things and Machine Learning, IoTML 2022 - Harbin, China Duration: 16 Dec 2022 → 18 Dec 2022

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12640
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	2022 International Conference on Internet of Things and Machine Learning, IoTML 2022
Country/Territory	China
City	Harbin
Period	16/12/22 → 18/12/22

Bibliographical note

Publisher Copyright:
© 2023 SPIE.

Keywords

Accuracy
Machine Learning
Patients Blood Samples

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1117/12.2673747

Cite this

DENG, Q., LYU, J., & ZOU, H. (2023). The Classification of Patient Blood Samples with Machine Learning Techniques. In H. WANG, & X. KONG (Eds.), Proceedings of SPIE : The International Society for Optical Engineering (Vol. 12640). Article 126401I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12640). SPIE. https://doi.org/10.1117/12.2673747

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title = "The Classification of Patient Blood Samples with Machine Learning Techniques",

abstract = "After implementing the Jaminan Kesehatan Nasional (JKN) in Indonesia, health system inequity, payment non-compliance and additional expenditure still exists. To better deal with the problems in their healthcare system, this study uses a variety of machine learning algorithms to classify patient blood samples for improving the efficiency of healthcare system. The study shows that most of the algorithms are up to 70% accuracy and the accuracy will rise with only important variables.",

keywords = "Accuracy, Machine Learning, Patients Blood Samples",

author = "Qian DENG and Jiayu LYU and Haowen ZOU",

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DENG, Q, LYU, J & ZOU, H 2023, The Classification of Patient Blood Samples with Machine Learning Techniques. in H WANG & X KONG (eds), Proceedings of SPIE : The International Society for Optical Engineering. vol. 12640, 126401I, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12640, SPIE, 2022 International Conference on Internet of Things and Machine Learning, IoTML 2022, Harbin, China, 16/12/22. https://doi.org/10.1117/12.2673747

The Classification of Patient Blood Samples with Machine Learning Techniques. / DENG, Qian; LYU, Jiayu; ZOU, Haowen.
Proceedings of SPIE : The International Society for Optical Engineering. ed. / Hongzhi WANG; Xiangjie KONG. Vol. 12640 SPIE, 2023. 126401I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12640).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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N2 - After implementing the Jaminan Kesehatan Nasional (JKN) in Indonesia, health system inequity, payment non-compliance and additional expenditure still exists. To better deal with the problems in their healthcare system, this study uses a variety of machine learning algorithms to classify patient blood samples for improving the efficiency of healthcare system. The study shows that most of the algorithms are up to 70% accuracy and the accuracy will rise with only important variables.

AB - After implementing the Jaminan Kesehatan Nasional (JKN) in Indonesia, health system inequity, payment non-compliance and additional expenditure still exists. To better deal with the problems in their healthcare system, this study uses a variety of machine learning algorithms to classify patient blood samples for improving the efficiency of healthcare system. The study shows that most of the algorithms are up to 70% accuracy and the accuracy will rise with only important variables.

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BT - Proceedings of SPIE : The International Society for Optical Engineering

A2 - WANG, Hongzhi

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T2 - 2022 International Conference on Internet of Things and Machine Learning, IoTML 2022

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The Classification of Patient Blood Samples with Machine Learning Techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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