title: Early detection of lung cancer risk using data mining.
abstract: BACKGROUND
  Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving.
  
  
  METHODS
  Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm.
  
  
  RESULTS
  Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer.
  
  
  CONCLUSIONS
  Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.
authors.affiliation: Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
authors.forename: Kawsar
authors.initials: K
authors.lastname: Ahmed
authors.forename: Abdullah Al
authors.initials: AA
authors.lastname: Emran
authors.forename: Tasnuba
authors.initials: T
authors.lastname: Jesmin
authors.forename: Roushney Fatima
authors.initials: RF
authors.lastname: Mukti
authors.forename: Md Zamilur
authors.initials: MZ
authors.lastname: Rahman
authors.forename: Farzana
authors.initials: F
authors.lastname: Ahmed
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001459:Bangladesh; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D003663:Decision Trees; D004198:Disease Susceptibility / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D055815:Young Adult