Biomedical Research

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Hybrid neuro-fuzzy system for prediction of stages of lung cancer based on the observed symptom values

The lung is an essential organ for humans, which often gets affected by diseases. Common lung diseases are asthma, tuberculosis and lung cancer. Among all these diseases, lung cancer is the deadliest one. Treatment of lung cancer depends on its staging. The objective of this work is to design a Hybrid Neuro- Fuzzy System (HNFS) for the prediction of stages of lung cancer, based on the observed symptom values. In order to predict its stages, the cancer assessment specific (stage-wise) questionnaire for lungs (CASQL) was prepared. The study was made by asking 167 lung cancer subjects and 50 normal subjects, aged between 37-81 years, to respond to the CASQ-L. The significant symptoms were identified based on Pearson’s correlations performed on all the observed data. Out of 217 subjects, 129 were used to train the system and other 88 were used for testing. The proposed hybrid system has achieved the highest classification accuracy of 97.7% and with the mean accuracy of 96.5% for a fivefold cross-validation analysis. Our findings suggest that the proposed HNFS would be useful for lung cancer stage-wise prediction.

Author(s): Thiyagarajan Manikandan, Nallaiyan Bharathi