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July 2017 Issue Vol.7 No.7
Lung Cancer Prediction System Using Data
Mining Classification Techniques in MedicalMalpractices
https://ia601506.us.archive.org/5/items/vol7no07/vol7no07.pdf
S.Karthigai
Ph.D., Research Scholar, Dept of Computer Science,Erode Arts And Science College (Autonomous), Erode,Tamil Nadu, India
Dr.K.Meenakshi Sundaram
Associate Professor, Dept of Computer Science,Erode Arts And Science College (Autonomous), Erode, Tamil Nadu, India
Abstract:
Cancer is the most important cause of death forboth men and women. The early detection of cancer can
be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of
cancer nodule in early stage is increasing. A disease that is commonly misdiagnosed is lung cancer. Earlier
diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing sudden
fatal end. Its cure rate and prediction depends mainly on the early detection and diagnosis of the disease. One of
the most common forms of medical malpractices globally is an error in diagnosis. Knowledge discovery
and data mining have found numerous applications in business and scientific domain. Valuable knowledge
can be discovered from application of data mining techniques in healthcare system. In this study, we briefly
examine the potential use of classification based data mining techniques such as Rule based, Decision tree,
Naïve Bayes and Artificial Neural Network to massive volume of healthcare data. The healthcare industry
collects huge amounts of healthcare data which,unfortunately, are not “mined” to discover hidden
information. For data preprocessing and effective decision making One Dependency Augmented Naïve
Bayes classifier (ODANB) and naive creedal classifier 2 (NCC2) are used. This is an extension of naïve Bayes to
imprecise probabilities that aims at delivering robust classifications also when dealing with small or
incomplete data sets. Discovery of hidden patterns and relationships often goes unexploited. Diagnosis of Lung
Cancer Disease can answer complex “what if” queries which traditional decision support systems cannot. Using
generic lung cancer symptoms such as age, sex, Wheezing, Shortness of breath, Pain in shoulder,chest, arm, it can predict the likelihood of patients getting
a lung cancer disease. Aim of the paper is to propose a model for early detection and correct diagnosis of the disease which will help the doctor in saving the life of the
patient.
Keywords:Lung cancer, Naive Bayes, ODANB, NCC2,Data Mining, Classification.
