Journal of Data Mining in Genomics & Proteomics

Abstract

Comparing the Efficiency of Diagnostic Models of Breast Cancer, Using Genetic Algorithm and Multi-Layer Perceptron Models

Hamid Moghaddasi, Reza Rabie and Solmaz Sohrabie*

Background and objective: Breast cancer has become a common cancer in women. The early diagnosis of breast cancer has beneficial effects on the life patients. Due to difficulties in the disease, data mining techniques could help to facilitate the diagnosis, the current study amid to compare the efficiency of Genetic Algorithm (GA) and Multi- Layer Perceptron (MLP) in the diagnosis of the breast cancer.

Methods: The database used in this paper is provided by Tehran university of Motamed Cancer Institute (MCI) breast cancer research center. This database included 7,625 records; there were 4,008 patients (52.4%) with breast cancers (malignant) and the remaining 3,617 patients (47.6%) without breast cancers (benign). GA and MLP models were developed using 14 fields (risk factor) of the database. The present study divided the data into 10 folds where 1 fold for testing and 9 folds for training as a way of validating the 10-fold crossover validation. Ultimately, the comparison of the models was made based on sensitivity, specificity, accuracy and ROC indicators.

Findings: Sensitivity, specificity, accuracy and ROC under curve of the MLP model were 0.815, 76.27, 79.71 and 81.24 respectively. For the GA model, the note indicators respectively reported: 0.884, 86.32, 87.67 and 88.50. There was statistical significant difference between indicators of the two models (p-value<0.0001).

Conclusion: Both models had acceptable efficiencies in diagnosing breast cancer, that GA had better efficiency. The number of breast cancer risk factors and number of database records can cause different sensitivity, specificity, accuracy and ROC indicators. More breast cancer risk factors such as mutation types could help to developing more efficient GA and ANN models.

Published Date: 2024-08-29; Received Date: 2020-11-02