Lung disease prediction system using data mining techniques

Abstract

Data mining is defined as analyzing very large amount of data for getting some useful information. Data mining techniques like association rule mining, classification and clustering is implemented to analyze the different types of disease. Classification is an important problem in Data mining. Given a database contains a collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records. Data mining plays an important role in medical systems. It is used to discover the knowledge out of data and presenting it in the form that human can easily understand. It is a cooperative effort of humans and computers. There are two primary goals of data mining - Prediction and Description. Prediction involves some variables or fields in the data set to predict unknown or future values of other variables of interest. Description focuses on finding patterns describing the data that can be interpreted by humans. It is very useful for predicting diseases such as Heart disease, Lung disease. Lung cancer is one of the most dangerous diseases in the world. The early detection of lung cancer can cure the disease completely. Data mining plays an effective role by using Naïve Bayes and Artificial Neural Network to massive volume of healthcare of data. The health care industry collects huge amounts of data which unfortunately are not mined to find the hidden data. The Naïve Bayes aims at delivering robust classifications also when dealing with small or incomplete data sets. The aim of the paper is to detect and diagnose the lung diesases as early as possible which will help the doctor to save the patient’s life. This paper describes how lung cancer was predicted and controlled, using data mining techniques.