Schaik, L. van (2013) Online diagnosesysteem voor gepigmenteerde huidaandoeningen. thesis, Medicine.
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Abstract
The purpose of this research is to develop an online diagnostic system for pigmented skin lesions, based on probability percentages. The literature shows that more and more people use the internet to search for information about their own health or others' health. There is also an increase in the use of interactive systems on the Internet. Increasingly, people are looking for their own medical diagnosis. Searching for health information has been years in development and for this reason there have been developed several diagnostic systems. Mostly, these diagnostic systems are intended to focus on overall health. All but one of these diagnostics aren’t based on probability percentages. The number of diagnostic systems for dermatological diagnosis is low and none of them are based on probability percentages. To respond to this need, there is an online diagnostic system developed based on probability percentages for pigmented skin lesions. The system consists of thirteen questions and some pictures. First, the a priori odds were estimated for the diagnoses. Then the probability percentages per diagnoses were estimated for each question and figure, and put in tables. These tables form the basis of the probability theory according to Bayes' theorem, on which the system is based. The results are displayed in a bar chart, with the rates per diagnosis. The system has been tested by twenty people on the skincancer campaign day. From this pilot shows that the system in two of the twenty cases suggested the correct diagnosis. The other results were not correct. This is due to an incorrect estimate of the probability percentages and especially an incorrect estimate of the a priori odds.
| Item Type: | Thesis (Thesis) |
|---|---|
| Supervisor name: | Fongers, A. and Jonkman, professor M.F. |
| Supervisor name: | Everdingen, J.J.E. van |
| Faculty: | Medical Sciences |
| Date Deposited: | 25 Jun 2020 10:57 |
| Last Modified: | 25 Jun 2020 10:57 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/1732 |
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