Predictive Analysis of Lung Cancer Recurrence.

The paper is about the predictive analysis of lung cancer recurrence based on non-small cell lung cancer carcinoma gene expression data using data mining and machine learning techniques. Prediction is one of the most significant factors in statistical analysis. Predictive analysis is a term describing a variety of statistical and analytical techniques used to develop models that predict future events or behaviours. Prediction of cancer recurrence has been a challenging problem for many researchers. The proposed method involves four phases: data collection, gene selection, designing classifier model, statistical parameter calculation and finally the comparison with previous results. The major part of the method is the gene selection and classification. A hybrid method for gene selection and classification is used for statistical analysis of lung cancer recurrence. The most suitable techniques are used for this work on the basis of comparative analysis of different classification method and optimization techniques.