Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Rania R. Kadhim
Mustansiriyah University
Economics, Econometrics & Finance

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of breast cancer classification models on Wisconsin dataset Rania R. Kadhim; Mohammed Y. Kamil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 11, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v11.i2.pp166-174

Abstract

Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images Rania R. Kadhim; Mohammed Y. Kamil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i1.pp9-18

Abstract

Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models' accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models' effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.
Co-Authors Mohammed Y. Kamil