title: A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.
abstract: 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.
authors.affiliation: Computer Engineering Department, Technological Educational Institute of Central Greece, 3rd KM Old National Road Lamia-Athens, 35100 Lamia, Greece. Electronic address: epapageorgiou@teiste.gr.
authors.forename: Elpiniki I
authors.initials: EI
authors.lastname: Papageorgiou
authors.affiliation: Computer Science Engineering, RVS College of Engineering and Technology, Coimbatore, India. Electronic address: jayashreecls@gmail.com.
authors.lastname: Jayashree Subramanian
authors.affiliation: Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore, India. Electronic address: skylabm@gmail.com.
authors.forename: Akila
authors.initials: A
authors.lastname: Karmegam
authors.affiliation: Diagnostic Medical Center "Diagnostiki A.E.", Larisa, Greece. Electronic address: nikpapandr@gmail.com.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Papandrianos
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D017143:Fuzzy Logic; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D057285:Precision Medicine / Q000379:methods*; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Familial breast cancer; Fuzzy Cognitive Maps; Hebbian learning; Medical decision support systems; Risk assessment