logistic regression

Breast cancer is the most common cancer among women and the existence of a precise and reliable system for the diagnosis of benign or malignant of this cancer is essential. Nowadays, using the results of needle aspiration cytology, data mining and machine learning techniques, early diagnosis of breast cancer can be done with greater accuracy. In this study, we propose a method consisting of two steps: in the first step, to eliminate the less important features, logistic regression has been used to select more important features. In the second step, the Support Vector Machine (SVM) classification algorithm has been used with three different kernel functions for the diagnosis of benign and malignant samples. To evaluate the performance of the proposed method, two data sets, WBCD and WDBC have been used with investigation of several metrics such as precision, the Area Under the ROC (AUC), true positive rate, false positive rate, accuracy and the F-measure. The results show that using the logistic regression method, it is possible to select the more efficient features, such that the proposed method reaches 98.69% in terms of classification accuracy.

Breast cancer is the second leading cause of death for women all over the world and since the cause of the disease remains unknown, the only method for controlling it is its early detection and diagnosis. The most prominent method for the treatment of breast cancer is biopsy and pathological tests. As the mentioned treatments are invasive and are, in many cases, unnecessary, researchers are in search for high-reliability computer-aided diagnostic systems in order to decrease the number of unnecessary biopsies. These systems consist of four major parts: preprocessing, segmentation, feature extraction and selection, and classification which are beneficial tools for diagnosis of breast cancer. In the present study in order to classify the breast tumors into benign and malignant, borders of the tumors are identified after image preprocessing using with a combination of manual and computerize approaches. In the next stage, 827 features, consisting of 24 shape-based morphological features and 803 border-based morphological features, have been extracted from each image, which 604 of them are recent features added in the present study. Subsequently, a sparse logistic regression classifier was used to eliminate the irrelevant features and classify the images. The data base used in the current study includes 104 Sonography images from breast tumors (72 from benign and 32 from malignant tumors). By applying the suggested algorithm in the present study to images, type of tumors was identified with 89.42% accuracy, 78.13% sensitivity, and 94.44% precision.