Repository logo
  • English
  • 中文
  • Log In
    New user? Click here to register.Have you forgotten your password?
Repository logo
    Communities & Collections
    Research Outputs
    Fundings & Projects
    People
    Organizations
    Statistics
  • English
  • 中文
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. TMU Publications / 北醫出版品
  3. .博碩士學位論文
  4. 109學年度
  5. 以資料探勘技術開採臨床膀胱癌數據並導入機器學習開發癌症早期預測模型
 
  • Details
Options

以資料探勘技術開採臨床膀胱癌數據並導入機器學習開發癌症早期預測模型

Other Title
Model for Prediction of Early Bladder Cancer by Exploration Clinical Data with Machine Learning Approach
Type
thesis
Date Issued
2021-07-01
Author(s)
李佳玲
Advisor
林景堉
Subjects
系所名稱:醫學檢驗暨生物技術學系碩士在職專班
Description
學位別:碩士
語文別:中文
指導教授:林景堉
口試委員:林景堉 LIN, CHING-YU;陳威戎 CHEN, WEI-JUNG;蘇家玉 SU, CHIA-YU
電子論文連結:https://handle.ncl.edu.tw/11296/yttre6
URI
https://203.71.86.71/handle/123456789/61606

Copyright Notice

● The digital content on this platform is part of the Taipei Medical University Institutional Repository, featuring various academic works and outputs from the institution. It offers free access to academic research and public education for non-commercial use.

● Please use the content appropriately and within legal boundaries to respect copyright owners' rights. For commercial use, please obtain prior authorization from the copyright owner. Users must not use TMUIR for any illegal purposes.

● By utilising the platform, users are deemed to have fully accepted and understood all the regulations set out in this statement, relevant laws of the Republic of China, all international internet regulations, and usage conventions.

● TMUIR is committed to protecting the interests of copyright owners. If you believe that any material on this website infringes copyright, please contact our staff at libirtmu@gmail.com, and we will remove the work from the repository.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback