Text mining has become a basic methodology for computational applications in the field of medical Reports. Text mining can be applied in the medical field for diagnosis of organs like Lung tumor, Head and neck, diabetes and other related diseases. Lung tumor is the most common disease, with more than one million cases being reported worldwide each year. The most effective way to reduce lung tumor deaths is by early diagnosis. This study aims to determine the lung tumor TNM Staging diagnosis. This research data uses National Cancer Institute (NCI) from UCI machine learning. Historical Medical Text Reports constitute a rich and varied source of information, which is today readily accessible, due to large-scale digitization efforts. But in spite of such large scale digitization efforts, stage data in Tumor (cancer) registries is often incomplete, inaccurate, or simply not collected. This paper describes a classification that automatically extracts Tumor staging information from the medical reports that identify malignant cases that are well suited for TNM staging using one class. This is because the decision is unaffected by the outliers and the form of the data fits more precisely. The system uses text classification techniques to extract elements of the stage listed in Tumor staging guidelines. When processing new reports, it classifies relevant sentences to help reach the staging decision and consequently, assigns the most likely stage. This Staging decision is appropriate for medical Text mining.

Text data mining, Tumor staging, Decision tree and PSO

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