Lung cancer transition rate by stages using discrete time markov model

Morbidity risk is linked to the health status or a disease within a population. Morbidity risk is the risk of illness associated with health status or disease within a population. Cancer is one of the prime causes of both morbidity and mortality in majority of countries worldwide. In year 2016, the...

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Published in:Indonesian Journal of Electrical Engineering and Computer Science
Main Author: Omar M.H.; Shair S.N.; Asmuni N.H.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079116917&doi=10.11591%2fijeecs.v18.i3.pp1295-1302&partnerID=40&md5=c2b82c28312a807f3f8617c2c18deb91
id 2-s2.0-85079116917
spelling 2-s2.0-85079116917
Omar M.H.; Shair S.N.; Asmuni N.H.
Lung cancer transition rate by stages using discrete time markov model
2020
Indonesian Journal of Electrical Engineering and Computer Science
18
3
10.11591/ijeecs.v18.i3.pp1295-1302
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079116917&doi=10.11591%2fijeecs.v18.i3.pp1295-1302&partnerID=40&md5=c2b82c28312a807f3f8617c2c18deb91
Morbidity risk is linked to the health status or a disease within a population. Morbidity risk is the risk of illness associated with health status or disease within a population. Cancer is one of the prime causes of both morbidity and mortality in majority of countries worldwide. In year 2016, the probability of Malaysian diagnosed with cancer before they reach age 75 is one over four. It has been reported that lung cancer has the highest deaths and it increased by 16.03% from 2012 to 2016. Malaysian National Cancer Registry reported that in year 2007 until 2011, 69.9% of lung cancer are men and the remaining 30.1% are women. Chinese become the dominant lung cancer cases representing 51.04% of total lung cancer patients in Malaysia followed by Malay, 44.81% and Indian, 4.16%. Treatments for lung cancer patients may vary by cancer stages. If cancer were just spread in one place, doctor may recommend a local treatment to get rid of cancer completely. Whereas, if a cancer has spread throughout the whole body, more comprehensive treatments may be needed. Therefore, knowing the probability of transition rate between cancer stages is important for healthcare cost effectiveness evaluation and expected cost calculation. This paper aims to estimate lung cancer transition rate by stages using the Functional Markov model. The Lung cancer transition rate will be calculated based on discrete time on a yearly basis. As a result, the probability of a Lung cancer patient recovering or deteriorating can be estimated. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
25024752
English
Article
All Open Access; Gold Open Access; Green Open Access
author Omar M.H.; Shair S.N.; Asmuni N.H.
spellingShingle Omar M.H.; Shair S.N.; Asmuni N.H.
Lung cancer transition rate by stages using discrete time markov model
author_facet Omar M.H.; Shair S.N.; Asmuni N.H.
author_sort Omar M.H.; Shair S.N.; Asmuni N.H.
title Lung cancer transition rate by stages using discrete time markov model
title_short Lung cancer transition rate by stages using discrete time markov model
title_full Lung cancer transition rate by stages using discrete time markov model
title_fullStr Lung cancer transition rate by stages using discrete time markov model
title_full_unstemmed Lung cancer transition rate by stages using discrete time markov model
title_sort Lung cancer transition rate by stages using discrete time markov model
publishDate 2020
container_title Indonesian Journal of Electrical Engineering and Computer Science
container_volume 18
container_issue 3
doi_str_mv 10.11591/ijeecs.v18.i3.pp1295-1302
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079116917&doi=10.11591%2fijeecs.v18.i3.pp1295-1302&partnerID=40&md5=c2b82c28312a807f3f8617c2c18deb91
description Morbidity risk is linked to the health status or a disease within a population. Morbidity risk is the risk of illness associated with health status or disease within a population. Cancer is one of the prime causes of both morbidity and mortality in majority of countries worldwide. In year 2016, the probability of Malaysian diagnosed with cancer before they reach age 75 is one over four. It has been reported that lung cancer has the highest deaths and it increased by 16.03% from 2012 to 2016. Malaysian National Cancer Registry reported that in year 2007 until 2011, 69.9% of lung cancer are men and the remaining 30.1% are women. Chinese become the dominant lung cancer cases representing 51.04% of total lung cancer patients in Malaysia followed by Malay, 44.81% and Indian, 4.16%. Treatments for lung cancer patients may vary by cancer stages. If cancer were just spread in one place, doctor may recommend a local treatment to get rid of cancer completely. Whereas, if a cancer has spread throughout the whole body, more comprehensive treatments may be needed. Therefore, knowing the probability of transition rate between cancer stages is important for healthcare cost effectiveness evaluation and expected cost calculation. This paper aims to estimate lung cancer transition rate by stages using the Functional Markov model. The Lung cancer transition rate will be calculated based on discrete time on a yearly basis. As a result, the probability of a Lung cancer patient recovering or deteriorating can be estimated. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 25024752
language English
format Article
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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