These errors can range from simple omissions to illegible hand writing and use of abbreviation, to inaccurate causes and manners of death.
These errors included minor errors like omission, illegible handwriting, and use of abbreviation and major errors like inaccurate Immediate cause of death (ICOD), Underlying cause of death (UCOD).
Moreover, the network and execution time prediction may be inaccurate, causing the performance of MCIoT systems to be degraded.
But in the dynamic environment, the results of them may become inaccurate causing excessive inventory (cost) or shortage.
Those who intentionally list inaccurate causes typically choose familiar ones, with the result that common causes of death appear even more common, and rare ones more rare.
Errors in mortality coding are frequently due to inaccurate completion of the cause-of-death section on death certificates (31 ).
Potential sources of bias in these estimates, including errors in population estimates, return migration, numerator denominator bias (e.g. when country of birth for an individual is recorded differently in the Census and on a death certificate), and inaccurate reporting of cause of death, were previously considered in more detail (Gill et al, 2002).
Inaccurate coding of cause of deaths is expected to be unrelated to exposure and would therefore create non-differential outcome misclassification resulting in an underestimation of any outcome-exposure association, if such an association is present.
Complaints about inaccurate certification of the cause of death for cancer patients in other countries are not only misplaced (because there is ample evidence of inaccurate certification in the UK [ 49, 50, 51, 52]), they are actually beside the point.
