A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system

Proper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.