An Early Diagnosis System for predicting Lung Cancer Risk Using adaptive Neuro Fuzzy Inference System and Linear Discriminant Analysis

Lung cancer is the number one cause of cancer deaths in both men and women worldwide. The general prognosis of lung cancer is poor because doctors tend not to and the disease until it is at an advanced stage. But it is very necessary to diagnose the disease early for taking preventive steps. In this paper, an adaptive Neuro Fuzzy Inference System (ANFIS) and Linear Discriminant Analysis (LDA) based lung cancer diagnosis system is proposed. This diagnosis system has mainly two steps: Feature extraction-reduction and classification First, lung cancer historical data sets are collected from different hospitals. They are then preprocessed. To reduce the lung cancer features dimensionality, Linear Discriminant Analysis (LDA) is applied. Reduced features are then fed into AN-FIS classifier system. classification accuracy, sensitivity and specificity analysis are performed for performance evaluation of proposed system. Obtained ac-curacy of about 95.4% shows that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.


Mustain Billah and Nazrul Islam

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