A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method

• Development of a familial breast cancer risk assessment model using Fuzzy Cognitive Map (FCM) methodology.
• The proposed methodology concentrates its efforts on personalized/individual decision making including the family history and the demographic risk factors to elicit the hidden risk of developing breast cancer.
• Hebbian-based learning capabilities of FCM were used to improve the modeling issue and contribute to risk prediction.
• The accuracy of the system is compared with benchmark machine learning methods showing its superiority.

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40–49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer–Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC.