Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease

Thyroid is an essential gland as its hormones are controlling the metabolism system of the human body. An abnormal amount of thyroid gland secretion causes two major types of diseases which are hyperthyroidism and hypothyroidism. In this research study, the implementation of K-Nearest neighbor (KNN) with its various distance functions is presented to detect thyroid disease. The proposed study consists of three phases, which are KNN without feature selection, KNN using L-1-based feature selection, and KNN using chi-square-based feature selection techniques. Thyroid datasets from KEEL dataset repository and another from a registered hospital in Pakistan were used in this study. The new dataset was distinguished from existing datasets as it included three additional features, i.e., pulse rate, Body Mass Index (BMI), and Blood Pressure (BP). Various distance functions were used to analyze the performance of the KNN model on these two datasets. Performance evaluation metrics have been used to discuss the achievement of the classifier. The optimal range of k values from the results are described between 1 and 5. Euclidean and Cosine distance functions achieved the highest accuracy using chi-square-based feature selection technique for new dataset as compared to existing datasets.