Efficient data mining techniques for medical data

Title

Efficient data mining techniques for medical data

Creator

Saleema, J S.

Contributor

Shenoy, P Deepa

Description

Healthy decision making for the well being is a challenge in the current era with abundant information everywhere. Data mining, machine newlinelearning and computational statistics are the leading fields of study that are supporting the empowered individual to take valuable decisions to optimize the outcome of any working domain. High demand for data newlinehandling exists in healthcare, as the rate of increase in patients is proportional to the rate of population growth and life style changes. Techniques for early diagnosis and prognosis prediction of diseases are the need of the hour to provide better treatment for the human community. Data mining techniques are a boon for building a quality and newlineefficient model for health prediction applications. As cancer explodes everywhere in recent years, the data sets from cancer newlineregistries have been focused as the medical data in this research. The main aim of thesis is to build a constructive and efficient classifier model for cancer prognosis prediction. Most of the existing system develops a diagnosis prediction models from the screening or survey data, as the data newlineset is widely available and are easy to collect due the insensitive nature of newlinethe factors involved in such research. Whereas the prognosis prediction requires a sensitive details of the patients those who are under treatment for a diagnosed disease. Hospitals and the community registries newlinemaintained by the government are the main source for data collection. Well maintained electronic hospital records with histopathology information is not public in India for the researchers. Hence cancer data newlinefrom a US based open access data center has been used in this research for all experimentation. This research work is a progressive model that gradually improves the newlineprediction accuracy by selecting appropriate data mining techniques in each phase.

Source

Author's Submission

Date

2018-01-01

Publisher

Christ(Deemed to be University)

Subject

Computer Science

Rights

Open Access

Relation

61000095

Format

PDF

Language

English

Type

PhD

Identifier

http://hdl.handle.net/10603/252043