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.

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 Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1677
Author(s):  
Ersin Elbasi ◽  
Ahmet E. Topcu ◽  
Shinu Mathew
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Bayes Classifier ◽  
Social Distancing ◽  
Public Areas ◽  
Iot Devices

COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.

Comparison of Machine Learning Algorithms in Breast Cancer Prediction Using the Coimbra Dataset

2019 ◽  
Author(s):  
Yolanda D Austria ◽  
Marie Luvett Goh ◽  
Lorenzo Sta. Maria Jr. ◽  
Jay-Ar Lalata ◽  
Joselito Eduard Goh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cancer Prediction

scholarly journals Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis

2021 ◽  
Vol 191 ◽  
pp. 487-492
Author(s):  
Mohammed Amine Naji ◽  
Sanaa El Filali ◽  
Kawtar Aarika ◽  
EL Habib Benlahmar ◽  
Rachida Ait Abdelouhahid ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cancer Prediction
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