scholarly journals Deep Neural Network Classifier in Breast Cancer Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 2106-2109
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Neural Network Classifier ◽  
Cancer Prediction ◽  
Neural Network Classifiers ◽  
Work First ◽  
Erroneous Data

The Breast cancer is the most life menacing disease among women. Early prophecy assurances the endurance of patients. In this work, first Deep neural network classifiers with different hidden layers with different nodes are used to explore the anthropometric information and blood investigation strictures and to predict the disease. Then machine learning algorithms such as SVM and Decision tree are also trained with the same data. Finally the performance of each classifier was deliberated. The pre-processed data of admitted patients with the breast cancer perception are used to train and test the classifiers. This article shack glow on the concert estimation based on right and erroneous data classification

scholarly journals Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

10.2196/17364 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17364 ◽  
Author(s):  
Can Hou ◽  
Xiaorong Zhong ◽  
Ping He ◽  
Bin Xu ◽  
Sha Diao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Deep Neural Network ◽  
Chinese Women ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Age At First Birth ◽  
Cancer Prediction

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

scholarly journals Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development (Preprint)

2019 ◽  
Author(s):  
Can Hou ◽  
Xiaorong Zhong ◽  
Ping He ◽  
Bin Xu ◽  
Sha Diao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Deep Neural Network ◽  
Chinese Women ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Age At First Birth ◽  
Cancer Prediction

BACKGROUND Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. OBJECTIVE This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. METHODS A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. RESULTS The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. CONCLUSIONS The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

scholarly journals Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Learning Platform

2020 ◽  
Vol 9 (2) ◽  
pp. 868-873
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Breast Tissue ◽  
Model Accuracy ◽  
Cancer Prediction ◽  
Learning Platform ◽  
Public Dataset ◽  
And Performance

Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XGBoost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.

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 Comparative Study of Machine Learning Algorithms for Breast Cancer Prediction - A Review

2019 ◽  
pp. 979-985
Author(s):  
Akshya Yadav ◽  
Imlikumla Jamir ◽  
Raj Rajeshwari Jain ◽  
Mayank Sohani
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Accurate Diagnosis ◽  
Machine Learning Algorithms ◽  
Bayes Classifier ◽  
Cancer Prediction ◽  
Short Span ◽  
Cancerous Cells

Cancer has been characterized as one of the leading diseases that causes death in humans. Breast cancer being a subtype of cancer causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. In this paper, different machine learning algorithms which can be used as tools by physicians for early and effective detection and prediction of cancerous cells have been studied and introduced. The different algorithms introduced here are ANN, DT, Random Forest (RF), Naive Bayes Classifier (NBC), SVM and KNN. These algorithms are trained with a dataset that contain parameters describing the tumor of a person having breast cancer and are then used to classify and predict whether the cell is cancerous.

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