An analysis on the impact of fluoride in human health (dental) using clustering data mining technique

• Published in: 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering pp. 370 - 375
• Main Authors: Balasubramanian, T., Umarani, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2012
• Subjects: Algorithm design and analysis; Clustering algorithms; Data mining; Dentistry; Diseases; Fluoride affected people; K-Means; Teeth; Water resources
• ISBN: 1467310379; 9781467310376
• DOI: 10.1109/ICPRIME.2012.6208374

Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Traditional data analysis methods often involve manual work and interpretation of data which is slow, expensive and highly subjective Data Mining, popularly called as knowledge discovery in large data, enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data. It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System. [1][2] This article explores data mining techniques in health care. In particular, it discusses data mining and its application in areas where people are affected severely by using the under-ground drinking water which consist of high levels of fluoride in Krishnagiri District, Tamil Nadu State, India. This paper identifies the risk factors associated with the high level of fluoride content in water, using clustering algorithms and finds meaningful hidden patterns which give meaningful decision making to this socio-economic real world health hazard.