The performance of an algorithm is usually measured in three dimensions (simplicity, processing time, and prediction power). In addition, we should take into account the noise resistance level in those measures. For this reason, this paper focuses on investigating the noisetolerance level of dual k-nearest neighbors (dual-kNN) primarily based on five noisy medical diagnosis problems. Literally, dual-kNN is a reborn version of the k-nearest neighbors (k-NN) algorithm with a new observation idea in the classification process with a collaborative effort between the first and second nearest neighbors of an observed instance. It was recently proven that dual-kNN has high prediction accuracy for a variety of real-world data sets, especially so in unbiased data sets. Thus, in this report, not only the prediction accuracy of dual-kNN is compared with normal k-NN, logistic regression, and the neural network, but we additionally investigate the noise tolerance in the aforementioned approaches. The practical data sets applied in this paper are medical data files from the University of California, Irvine, Machine Learning Repository. In this report, the new approach to dual-kNN commences with better prediction accuracy, and higher noise resistance is presented, in comparison with normal k-NN, logistic regression, and neural networks.