New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

Table 2

Patient and feature information of the datasets.

Datasets	Patient number ()	Feature number ()	Features
Raw dataset	2070	125	Dia, age, sex, CS, PS, HG, MS, NI, ER, PR, HER2, RS, NOBCIF, NOOCIF, NOOTIF, NOTCIF, GR, S, AI, MG, P, LS, M, OC, HI, BRCA etc.
Dataset 1	1460	26	Dia, age, sex, CS, PS, HG, MS, NI, ER, PR, HER2, RS, NOBCIF, NOOCIF, NOOTIF, NOTCIF, GR, S, AI, MG, P, LS, M, OC, HI, BRCA
Dataset 2	1532	9	Dia, age, FIB, SEB, THB, , FIO, , BRCA

Dia: diagnosis; CS: clinical stage; PS: pathological stage; HG: histological grades; MS: metastasis status; NI: node involvement; ER: estrogen receptor; PR: progesterone receptor; RS: receptor status; NOBCIF: number of breast cancer in family; NOOCIF: number of ovarian cancer in family; NOOTIF: number of other tumors in family; NOTCIF: number of total cancer in family; FIB: number of first degree relative in breast; SEB: number of second degree relative in breast; THB: number of third degree relative in breast; : number of third degree relative in breast under age of 40; FIO: number of first degree relative in ovarian; : number of first degree relative in ovarian above age of 40; GR: geographical regions; S: smoking; AI: alcohol intake; MG: mammography; P: pregnancy; LS: lactation status; M: menopause; OC: oral contraceptive; HI: hormone intake.