Comparative Study of Machine Learning Algorithms Using the Breast Cancer Dataset

2020 ◽  
pp. 83-91 ◽  
Author(s):  
Houssam Benbrahim ◽  
Hanaâ Hachimi ◽  
Aouatif Amine
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Breast Cancer Dataset ◽  
Cancer Dataset

Breast Cancer Prediction and Classification Using Supervised Learning Techniques

2020 ◽  
Vol 17 (6) ◽  
pp. 2519-2522
Author(s):  
Kalpna Guleria ◽  
Avinash Sharma ◽  
Umesh Kumar Lilhore ◽  
Devendra Prasad
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Breast Cancer Dataset ◽  
Cancer Awareness ◽  
Cancer Dataset ◽  
Cancer Prediction

Approximately 2.1 million women every year are affected due to breast cancer which has become one of the major causes for cancer related deaths among women. World Health Organization’s (WHO) report 2018, reveals that around 15% of deaths among women are due to breast cancer. Lack of awareness is one of the major reason which has led to the detection of breast cancer at the later stage. Another major reason is access to limited health resources which make the problem worse. Early or timely detection of breast cancer is utmost important to increase the survival rate of the patients. World Health Organization’s (WHO) cancer awareness guidelines recommend that women aged between 40–49 years of age or 70–75 years of age must be subjected to mammographic screening which will provide the timely detection of the problem, if it persist. This article uses Breast Cancer dataset from UCI machine learning repository to predict and diagnose the class of breast cancer: benign or malignant by using supervised learning. Supervised machine learning algorithms: KNearest Neighbor (K-NN), Naive Bayes, logistic regression and decision tree have been utilized for breast cancer prediction. The performance evaluation of these classification algorithms is done based on various performance measures: accuracy, sensitivity, specificity and F -measure.

scholarly journals Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets

2021 ◽  
Vol 23 (11) ◽  
pp. 749-758
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cancer Prognosis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Human Beings ◽  
Original Dataset

Breast Cancer is one of the chronic diseases occurred to human beings throughout the world. Early detection of this disease is the most promising way to improve patients’ chances of survival. The strategy employed in this paper is to select the best features from various breast cancer datasets using a genetic algorithm and machine learning algorithm is applied to predict the outcomes. Two machine learning algorithms such as Support Vector Machines and Decision Tree are used along with Genetic Algorithm. The proposed work is experimented on five datasets such as Wisconsin Breast Cancer-Diagnosis Dataset, Wisconsin Breast Cancer-Original Dataset, Wisconsin Breast Cancer-Prognosis Dataset, ISPY1 Clinical trial Dataset, and Breast Cancer Dataset. The results exploit that SVM-GA achieves higher accuracy of 98.16% than DT-GA of 97.44%.

scholarly journals Deep Convolution Neural Network Based Breast Cancer Bigdata Analysis for Crowd Cloud Sourcing

2020 ◽  
Vol 9 (3S) ◽  
pp. 378-383
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Medical Industry ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Major Surgery ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Deep Convolution Neural Network

Breast cancer is one of the dangerous diseases leads fast death among women. Several kinds of cancers are affecting people, but breast cancer affects highly women. In medical industry removal of women breasts or major surgery is taken forward as the solution, where it reoccurs after surgery also. Only solution to save women from breast cancer is to identify and detect the earlier stage of cancer and provide necessary treatment. Hence various research works have been focused on finding good solution for diagnosing and classifying the cancer stages as benign, malignant or severe malignant. Still the accuracy of classification needs to be improved on complex breast cancer datasets. Few of the earlier research works have proposed machine learning algorithms, which are semiautomatic and accuracy is also not high. Thus, to provide a better solution this paper aimed to use one of the deep learning algorithms such as Convolution Neural Networks for diagnosing various kinds of breast cancer dataset. From the experimental results, it is obtained that the proposed deep learning algorithms outperforms than the other algorithms.

Sign in / Sign up

Export Citation Format

Share Document