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Minimizing breast cancer symptomatol...

Besse, Anna Marie.

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Minimizing breast cancer symptomatology in recurrence prediction using artificial neural networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Minimizing breast cancer symptomatology in recurrence prediction using artificial neural networks./
作者:	Besse, Anna Marie.
面頁冊數:	40 p.
附註:	Source: Masters Abstracts International, Volume: 42-03, page: 0957.
Contained By:	Masters Abstracts International42-03.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1417027
ISBN:	0496218115

Minimizing breast cancer symptomatology in recurrence prediction using artificial neural networks.
Besse, Anna Marie.

Minimizing breast cancer symptomatology in recurrence prediction using artificial neural networks. - 40 p.

Source: Masters Abstracts International, Volume: 42-03, page: 0957.

Thesis (M.Eng.)--University of Louisville, 2003.

This thesis pursues the objective of helping doctors make critical decisions about breast cancer patients by using artificial neural networks to reduce the number of symptoms the doctor must evaluate in making diagnostic decisions. By narrowing the number of symptoms that must be taken into consideration, we can help reduce the complexity of the process and improve the quality of the diagnostic process overall.

ISBN: 0496218115Subjects--Topical Terms:

626642
Computer Science.

Minimizing breast cancer symptomatology in recurrence prediction using artificial neural networks.
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502 $a Thesis (M.Eng.)--University of Louisville, 2003.
520 $a This thesis pursues the objective of helping doctors make critical decisions about breast cancer patients by using artificial neural networks to reduce the number of symptoms the doctor must evaluate in making diagnostic decisions. By narrowing the number of symptoms that must be taken into consideration, we can help reduce the complexity of the process and improve the quality of the diagnostic process overall.
520 $a At the beginning of the project clinical information on 492 breast cancer patients was acquired for study. Each patient's data included information such as age, symptom measurements, and whether or not the patient experienced relapse. The first step in the project was to complete pre-processing of the data and put it in a useful format for the Stuttgart Neural Network Simulation software package to manipulate and extract results. Once pre-processing was completed the process of discovery began. (Abstract shortened by UMI.)
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