Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support

The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: $\mathbf{R}^{2}$, MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.