Predictive Data Mining for Oral Cancer Treatment

2014 ◽  
pp. 303-328 ◽  
Author(s):  
Neha Sharma ◽  
Hari Om
Keyword(s):  
Data Mining ◽  
Oral Cancer ◽  
Cancer Treatment ◽  
Predictive Data Mining

scholarly journals PPARγ targeted oral cancer treatment and additional utility of genomics analytic techniques

2016 ◽  
Vol 127 (4) ◽  
pp. E124-E131 ◽  
Author(s):  
Nathan Handley ◽  
Jacob Eide ◽  
Randall Taylor ◽  
Beverly Wuertz ◽  
Patrick Gaffney ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Cancer Treatment

Oral cancer treatment and immune targets – A role for dendritic cells?

2012 ◽  
Vol 40 (2) ◽  
pp. 103-104 ◽  
Author(s):  
Hans-Robert Metelmann ◽  
Peter Hyckel ◽  
Fred Podmelle
Keyword(s):  
Dendritic Cells ◽  
Oral Cancer ◽  
Cancer Treatment

Omics Data Mining for multiPTMs in Oral Cancer: Cellular Proteome and Secretome of Chronic Tobacco-Treated Oral Keratinocytes

2021 ◽  
Author(s):  
Akhina Palollathil ◽  
Anjana Aravind ◽  
Manavalan Vijayakumar ◽  
Chinmaya Narayana Kotimoole ◽  
Varshasnata Mohanty ◽  
...  
Keyword(s):  
Data Mining ◽  
Oral Cancer ◽  
Omics Data ◽  
Oral Keratinocytes

Nanotechnology in Oral Cancer Treatment

2021 ◽  
pp. 369-379
Author(s):  
Chandramani B. More ◽  
Rahi M. Brahmbhatt ◽  
Naman R. Rao
Keyword(s):  
Oral Cancer ◽  
Cancer Treatment

Overview of Predictive Modeling Approaches in Health Care Data Mining

2016 ◽  
pp. 73-95 ◽  
Author(s):  
Sunita Soni
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Clinical Information ◽  
Ease Of Use ◽  
Classification Model ◽  
Computational Time ◽  
Data Sets ◽  
Advantages And Disadvantages ◽  
Mining Methods ◽  
Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

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