We analyze the discriminatory power of k-nearest neighbors, logistic examples. A variety of statistical and machine learning apregression, artificial neural networks (ANNs), decision tress, and supproaches to classification tasks are currently available, but port vector machines (SVMs) on the task of classifying pigmented few comparisons among different models have been done skin lesions as common nevi, dysplastic nevi, or melanoma. Three on the same data sets. Although the potential advantages different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melaand disadvantages of utilizing each of these methods have noma, the dichotomous problem of distinguishing melanoma from been defined theoretically, given certain assumptions about common and dysplastic nevi, and the trichotomous problem of correctly data distribution, characteristics of the classification task, distinguishing all three classes. Using ROC analysis to measure the signal-to-noise ratio, etc., it is often the case that these asdiscriminatory power of the methods shows that excellent results for sumptions cannot be verified in practice. Under these cirspecific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous cumstances, empirical comparison of classification perforand trichotomous tasks, logistic regression, ANNs, and SVMs permance using standard metrics to describe discrimination and final selection of the "best model" for a given classification task can only be concluded after considering the tradeoffs between classification performance, costs, and model interpretability.
After feature extraction, machine learning methods such as k-nearest neighbors (kNN), Artificial Neural Networks (ANNs), logistic regression, decision trees and support vector machines (SVMs) can be used to perform the classification task with moderate success REF .
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions
Journal of Biomedical Informatics
Computer Science Medicine