Document Type : Original Article
Authors
- . Mohammad R. Afrash 1
- . Azadeh Bayani 1
- . Mostafa Shanbehzadeh 2
- . Mohammadkarim Bahadori 3
- . Hadi Kazemi‑Arpanahi 4
1 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
3 Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
4 Department of Health Information Management and Technology, Abadan University of Medical Sciences, Abadan, Iran
Abstract
BACKGROUND: Breast cancer (BC) is the most common cause of cancer‑related deaths in women
globally. Currently, many machine learning (ML)‑based predictive models have been established to
assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation
even with having healthy lifestyle behaviors or preventing disease at early stages can significantly
lead to optimal population‑wide BC health. Thus, we aimed to develop a prediction model by using a
genetic 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 records
in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a
modified 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 system
based 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. The
experimental results showed that the decision tree yielded a superior performance than other ML
models, 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 elevated
risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and
serve as an important tool for developing preventive health strategies.
Keywords
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