The Development of a Fuzzy Logic System Using MATLAB for Early Detection of Hereditary Cancer in BRCA1/2 Negative Cases

Senturk, N; Volkan, GP; SM, Babiker Ali; Dogan, B; Aliyeva, L; Sag, OS; Temel, G S; Dundar, M; Ergoren, C M

The Development of a Fuzzy Logic System Using MATLAB for Early Detection of Hereditary Cancer in BRCA1/2 Negative Cases

Balkan Journal of Medical Genetics

Volume 28 (2025): Issue 1 (June 2025)

By:

N Senturk, GP Volkan, Babiker Ali SM, B Dogan, L Aliyeva, OS Sag, G S Temel, M Dundar and C M Ergoren

Open Access

|Oct 2025

Figures & Tables

Distribution of the tumor location. 38% (34 patients) had a tumor on their left breast alone, 29% (26 patients) had a tumor on their right breast alone, and 7% (six patients) bilateral.

Gene variant distributions according to ACMG categories (P: pathogenic, LP: likely pathogenic, LB: likely benign, B: benign, VUS: variant with unknown significance).

Representation of the fuzzy logic model in MATLAB, showing how 14 clinical input parameters are mapped to a single output classification. The membership functions assign degrees of belonging to each input, with values ranging from 0 to 1.

Rules section in the Fuzzy Logic System, utilized data from 90 patients and parameters as input and membership functions within the rule section.

The set of outputs within the fuzzy logic interface on MATLAB. showing classification intervals from 0 to 1. A value of 1 corresponds to ‘Pathogenic’, 0.75 to ‘Likely pathogenic’, 0.5 to ‘VUS’, 0.25 to ‘Likely benign’, and 0 to ‘Benign’, based on ACMG classification criteria.

The second example of patient data_

Input Parameters	Sample Patient Data
Age	50
Sex	Female
Consanguinity	No
Family History	Yes
Membership Degree	1
Tumor Size	16cm
Lymph Node	No
Malignancy	Unknown
Location	Left Breast
Estrogen Receptor	Positive
Progesterone	Positive
Gene Variation	ATM
Diagnosis	Positive
Classification	VUS

The first example of patient data_

Input Parameters	Sample Patient Data
Age	51
Sex	Female
Consanguinity	No
Family History	Yes
Membership Degree	unknown
Tumor Size	10cm
Lymph Node	No
Malignancy	2
Location	Both Breast
Estrogen Receptor	Positive
Progesterone	Positive
Gene Variation	APC
Diagnosis	Positive
Classification	Likely Benign

Output cluster and Values of Membership_

Membership Functions of Output Classification	Values of Membership Functions
Benign	0
Likely Benigh	0.25
VUS (Variant with Unknown Significance)	0.5
Likely Pathogenic	0.75
Pathogenic	1

System validation results

Risk factors	Patient 1	Patient 2	Patient 3	Patient 4	Patient 5	Patient 6
Age	42	42	34	52	47	49
Sex	Female	Female	Female	Female	Female	Female
Consanguinity	Unknown	No	Unknown	Unknown	Yes	Unknown
Family History	Positive	Positive	Positive	Positive	Negative	Positive
Tumor Size	Unknown	1.9x1.8	3x2.5	2x2	1.2x0.5	3x2x2
Membership Degree	Grade 2	Grade 3	Grade 2	Grade 2	Grade 2	Grade 3
Location	Right Breast	Right Breast	Right Breast	Left Breast	Right Breast	Left Breast
Estrogen receptor	Positive	Positive	Positive	Positive	Positive	Negative
Progesterone	Positive	Negative	Positive	Positive	Positive	Negative
Gene/Gene Variation	Encodes Nibrin NBN	Double Strand Break Repair Protein RAD50	Double Strand Break Repair Protein RAD50	DNA Mismatch Repair Protein MSH6	Adenomatosis Polyposis Coli APC	DNA Mismatch Repair Protein MSH2
Variant	c.1154_1155del	c.2014C>T	c.980G>A	c.663A>C	c.296G>A	c.435T>G
Diagnose	YES	YES	YES	YES	YES	YES
Classification	P	P	LB	LB/CIP	VUS	VUS
Output	P	P	LB	LB/CIP	VUS	VUS
Percentage Of Results	0.92 %92	0.92 %92	0.25 %25	0.5 %50	0.5 %50	0.5 %50

Membership function values in each input cluster for each risk factor_

Risk Factors	Membership Functions	Values
Age	<15	0
	16–29	0.25
	30–39	0.5
	40–59	0.75
	>=60	1
Sex	Female	1
Sex	Male	0
Consanguinity	Yes	1
Consanguinity	No	0
Family History	Yes	1
Family History	No	0
Membership Degree	0	0
	1 & 2	0.5
	>=3	1
Tumor Size	0–19cm	0
	20–39cm	0.5
	>=40cm	1
Lymph Node	Yes	1
Lymph Node	No	0
Malignancy	Grade 1	0
	Grade 2	0.5
	Grade 3	1
Location	Other	0.25
	Right Breast	0.5
	Left Breast	0.75
	Both Breast	1
Estrogen Receptor	Positive	1
Estrogen Receptor	Negative	0
Progesterone	Positive	1
Progesterone	Negative	0
Gene Variation	TP53	0.1
	FAM175	0.15
	RAD50	0.2
	NBN	0.25
	MSH6	0.3
	APC	0.35
	MSH2	0.4
	ATM	0.45
	CDH1	0.5
	MUTY	0.55
	PALB2	0.6
	BLM	0.65
	MRE11A	0.7
	PMS2	0.75
	CHEK2	0.8
	PTEM	0..85
	BART1	0.9
	BRIP	1
Diagnosis	Yes	1
Diagnosis	No	0
Classification input and output	Benign (B)	0
	Likely Benign (LB)	0.25
	VUS	0.5
	Likely Pathogenic (LP)	0.75
	Pathogenic(P)	1

The third example of patient data_

Input Parameters	Sample Patient Datas
Age	62
Sex	Female
Consanguinity	No
Family History	Yes
Membership Degree	1
Tumor Size	20cm
Lymph Node	Unknown
Malignancy	Unknown
Location	Right Breast
Estrogen Receptor	Negative
Progesterone	Negative
Gene Variation	RAD50
Diagnosis	Positive
Classification	Pathogenic

DOI: https://doi.org/10.2478/bjmg-2025-0011 | Journal eISSN: 2199-5761

Journal RSS Feed

Language: English

Published on: Oct 8, 2025

Published by: Macedonian Academy of Sciences and Arts

In partnership with: Paradigm Publishing Services

Publication frequency: 2 times per year

Keywords:

Related subjects:

Basic medical science,

Basic medical science, other

© 2025 N Senturk, GP Volkan, Babiker Ali SM, B Dogan, L Aliyeva, OS Sag, G S Temel, M Dundar, C M Ergoren, published by Macedonian Academy of Sciences and Arts
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous article Volume 28 (2025): Issue 1 (June 2025)Next article

The Development of a Fuzzy Logic System Using MATLAB for Early Detection of Hereditary Cancer in BRCA1/2 Negative Cases

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

The second example of patient data_

The first example of patient data_

Output cluster and Values of Membership_

System validation results

Membership function values in each input cluster for each risk factor_

The third example of patient data_

Paradigm

My account