. Mohammad R. Afrash; . Azadeh Bayani; . Mostafa Shanbehzadeh; . Mohammadkarim Bahadori; . Hadi Kazemi‑Arpanahi
Volume 12, Issue 7 , August 2022, , Pages 1-12
Abstract
BACKGROUND: Breast cancer (BC) is the most common cause of cancer‑related deaths in womenglobally. Currently, many machine learning (ML)‑based predictive models have been established ...
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BACKGROUND: Breast cancer (BC) is the most common cause of cancer‑related deaths in womenglobally. Currently, many machine learning (ML)‑based predictive models have been established toassist clinicians in decision making for the prediction of BC. However, preventing risk factor formationeven with having healthy lifestyle behaviors or preventing disease at early stages can significantlylead to optimal population‑wide BC health. Thus, we aimed to develop a prediction model by using agenetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC.MATERIAL AND METHODS: The data of 3168 healthy individuals and 1742 patient case recordsin the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, amodified hybrid GA was used to perform feature selection and optimization of selected features.Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward,the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity,and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support systembased on the best model was developed.RESULTS: After performing feature selection, age, consumption of dairy products, BC family history,breast biopsy, chest X‑ray, hormone therapy, alcohol consumption, being overweight, having children,and education statuses were selected as the most important features for prediction of BC. Theexperimental results showed that the decision tree yielded a superior performance than other MLmodels, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively.CONCLUSION: The developed predictive system can accurately identify persons who are at elevatedrisk for BC and can be used as an essential clinical screening tool for the early prevention of BC andserve as an important tool for developing preventive health strategies.
