Selecting Classifiers for Medical Data Analysis
- Resource Type
- Conference
- Authors
- Abin, D.; Potey, M.A.
- Source
- 2013 International Conference on Machine Intelligence and Research Advancement Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on. :285-289 Dec, 2013
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Data mining
Medical diagnostic imaging
Diseases
Classification algorithms
Decision trees
Standards
Cancer
Training set
test data
decision tree
error estimates
- Language
Applying data mining algorithms for knowledge discovery is being done in almost all domains but particularly in medical and healthcare domain it is still a challenge. Health issues are a major concern in the current world. Massive amount of high dimensional data is generated across the medical organizations. To discover knowledge for building efficient healthcare systems data mining algorithms play a crucial role. We have applied selected Decision tree classification algorithms for performing medical data analysis which is important in medical sector. We suggest appropriateness of the selected algorithms to the specific disease data.