Breast cancer is a tumor caused by the excessive growth of cells in the breast tissue or near the region of the breast. Breast cancer is most commonly found among women, and it starts developing when the cell tissues from lump of the breast become abnormal or when there is a calcium deposit in the breast. These affected cells form a large lump that consequently becomes a tumor. Digital infrared images are obtained based on the metabolism of the breast and vascular circulation of the blood flow in and around the breast region, which has more visibility than the normal breast region. In this article, we diagnose breast cancer by processing a thermal image that is acquired from thermal cameras. By analyzing the information, we can implement image processing steps to predict quantitative and qualitative information. In this work, we propose a hybrid clustering algorithm with distance measurements. The clustering step includes adaptive fuzzy k-means clustering with Chebyshev distance with improved classifiers, which include neural networks. The article discusses the experimental results along with the comparison using various metrics, such as accuracy, time, and error rates.

Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20259998. Data mining is one of the most promising areas of research that has become increasingly popular in health care. The objective of this research article is to elucidate a transdisciplinary approach to classify thyroid levels in patients using data mining techniques. The data set consisting of more than 2 thyroid conditions along with the normal values listed 21 values. The classifier chosen carefully to get the optimized accuracy and falls in different classification category namely J48, random forest, and random tree from tree, decision table from rules, multilayer perceptron from functions, naïve Bayesian from Bayes, and AdaBoost from meta respectively. The J48 classifier displays tree that will assist with better interpretation based on the values and helps to easily determine new value combinations. The Thyropred System graphic is finally presented, which guides the diagnosis of thyroid disease. Thorough consultation with experts along with this prediction system guides the decision of further medication. The J48 classifier provides the best accuracy when compared with the other tested classifiers.