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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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-06-01.
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| LEADER | 02848 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs21501_13754 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hakeem Omar, Muhammad |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Niza Shair, Syazreen |e author |
| 700 | 1 | 0 | |a Haniah Asmuni, Nurin |e author |
| 245 | 0 | 0 | |a Lung cancer transition rate by stages using discrete time markov model |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21501 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Cancer transition rate ; Functional markov model; Lung cancer; Markov model; Morbidity risk | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 3: June 2020; 1295-1302 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21501/13754 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21501/13754 |z Get fulltext |