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   prediction of breast cancer using machine learning approaches  
   
نویسنده rabiei reza ,ayyoubzadeh mohammad ,sohrabei solmaz ,esmaeili marzieh ,atashi alireza
منبع journal of biomedical physics and engineering - 2022 - دوره : 12 - شماره : 3 - صفحه:297 -308
چکیده    Background: breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. machine learning has the potential to predict breast cancer based on features hidden in data. objective: this study aimed to predict breast cancer using different machinelearning approaches applying demographic, laboratory, and mammographic data. material and methods: in this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from motamed cancer institute (acecr), tehran, iran. the database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. the random forest (rf), neural network (mlp), gradient boosting trees (gbt), and genetic algorithms (ga) were used in this study. models were initially trained with demographic and laboratory features (20 features). the models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. results: rf presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (auc) 0.56). gradient boosting (auc=0.59) showed a stronger performance compared to the neural network. conclusion: combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
کلیدواژه artificial intelligence ,breast cancer ,computing methodologies ,genetic algorithm ,machine learning
آدرس shahid beheshti university of medical sciences, school of allied medical sciences, department of health information technology and management, iran, tehran university of medical science, school of allied medical sciences, department of health information technology and management, iran, zanjan university of medical sciences, office of statistic and information technology management, department deputy of development, management and resources, iran, tehran university of medical science, school of allied medical sciences, department of health information technology and management, iran, tehran university of medical sciences, virtual school, department of e-health, iran. academic center for education, culture and research (acecr), breast cancer research center, motamed cancer institute, medical informatics research group, clinical research department, iran
پست الکترونیکی smatashii@gmail.com
 
     
   
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