A new hybrid method based on fuzzy-artificial immune system and k -nn algorithm for breast cancer diagnosis

• Published in: Computers in biology and medicine Vol. 37; no. 3; pp. 415 - 423
• Main Authors: Şahan, Seral, Polat, Kemal, Kodaz, Halife, Güneş, Salih
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2007
• Subjects: [formula omitted]-Fold cross validation; [formula omitted]-Nearest neighbour classification system; Algorithms; Artificial immune systems; Artificial Intelligence; Biopsy, Needle; Breast - pathology; Breast cancer diagnosis; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Breast Neoplasms - immunology; Breast Neoplasms - pathology; Data Collection; Decision Making, Computer-Assisted; Diagnosis, Computer-Assisted; Female; Fuzzy Logic; Fuzzy weighting; Humans; Immune System - immunology; Internal Medicine; Other; Registries; Software; Wisconsin Breast Cancer Diagnosis Data; k -Nearest neighbour classification system; Fuzzy weighting; k -Fold cross validation; Artificial immune systems; Breast cancer diagnosis; Wisconsin Breast Cancer Diagnosis Data
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2006.05.003

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k -nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.