Lung Cancer Examination and Risk Severity Prediction using Data Mining Algorithms
Keywords:
Classification and Regression Tree, Detection, Fuzzy Logic, K-Nearest Neighbor, Particle Swarm Optimization, Profuse Clustering, Segmentation, Severity PredictionAbstract
Lung cancer is one among the primary wellsprings of disease passing, worldwide and even more particularly in India. As the symptoms of the lung cancer can't show explicitly, early disclosure of lung tumor is enchant; the endurance pace of lung disease is more, if it is found early. To propel the early finding of lung tumor, the patient should insight screening quickly after the secondary effects are taking note. In this paper, a construction for risk seriousness expectation of lung cancer is proposed to further develop the forecast exactness of the lung tumor. To improve the nature of the image and to remove the commotion by proposed Profuse Grouping Algorithm for Image Denoising. After completion denoising stage, the denoised images are tested with Enhanced k-nearest neighbor method for detecting the cancer. To increase the segmentation process Advanced Classification and Regression Tree algorithm is used to segment the lung cancer properly. At last, Fuzzy logic method has been used to find the detection level of the lung cancer and to identify the risk severity of the lung.
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