Thyroid Diseases Forecasting Using a Hybrid Decision Support System Based on ANFIS, k-NN and Information Gain Method

New statistical analysis and data mining techniques are utilized by researchers to develop tools that help healthcare professionals to easily and efficiently diagnose thyroid related diseases. Useful knowledge can be extracted from the databases where a significant amount of relevant data is stored. A new decision-based hybrid system for the diagnosis of thyroid diseases is presented in this article. The proposed system consists of three stages. in the first stage, 25 features of the dataset (retrieved from the University of California Irvin machine learning repository) were reduced using Information Gain method to avoid data redundancy and reduce computation time. In the second stage, the missing values in the dataset are dealt with k-Nearest Neighbor (k-NN) weighting pre-processing scheme. Finally, the resultant data is provided as input to Adaptive Neuro-Fuzzy Inference System for the purpose of inputoutput mapping in the last stage of our proposed system. Classification accuracy for the proposed approach was calculated to be99.1%, whereas sensitivity and specificity results were 94.77% and 99.70%, respectively. Our approach is able to get highest classification accuracy with minimum possible features of the dataset and can be applied to diagnose other lethal diseases.