Various cancer classification techniques have been developed in the past but most of them are based on the clinical analysis of morphological symptoms (Hong & Cho, 2004) and with such methods, even a trained specialist may make diagnostic errors. Experiments were done on several benchmark datasets such as the leukemia dataset, the lymphoma dataset, ovarian cancer dataset, and breast cancer dataset.ĭifferent types of cancer may have similar symptoms and an accurate classification of the type of cancer is thus necessary in order to treat a patient properly. Furthermore, the results from the proposed method show considerably better performance than other algorithms. Thus, the proposed approach is a novel method for the cancer classification. A case-based classifier approach together with the genetic algorithm has never been applied before for the classification of cancer, same with the application of the Mahalanobis distance. The authors have also used the well-known Mahalanobis distance of multivariate statistics as a similarity measure that improves the accuracy. This paper proposes a new method, combining the genetic algorithm, case-based reasoning, and the k-nearest neighbor classifier, which improves the performance of the classification considerably. The DNA microarray technique (also called the DNA chip) has been used in the past for the classification of cancer but it generates a large volume of noisy data that has many features, and is difficult to analyze directly. In addition, it is a technical challenge to classify accurately the cancer cells in order to differentiate one type of cancer from another. AbstractThere are many similarities in the symptoms of several types of cancer and that makes it sometimes difficult for the physicians to do an accurate diagnosis.

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