Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine

K Gowri Subadra; P Sathish Babu

doi:10.2478/msr-2025-0020

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Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine

Measurement Science Review

Volume 25 (2025): Issue 4 (August 2025)

By: K Gowri Subadra and P Sathish Babu

Open Access

|Jul 2025

Figures & Tables

Proposed breast cancer detection using GA and SVM.

Table 1.

Selection models for cancer detection based on the area under the curve.

S. No.	Features	Selection of model (Intermediate selection)	Selection of features (Eventual selection)
1.	8LTP + Wavelets + Fractals	81.12	95.87
2.	8LTP + Fractals	81.12	97.16
3.	GLCM	81.12	95.38
4.	2LTP + Fractals + GLCM	76.00	84.98
5.	3LTP + Fractals	74.71	84.98
6.	8LTP + GLCM	69.80	97.16

[i] Note: GLCM – grey level co-occurrence matrix

Table 2.

Performance evaluation of different feature groups.

S. No.	Features	F1 score [%]	Accu [%]	Sensy [%]	Specy [%]
1.	8LTP + Wavelets + Fra	95.88	95.62	98.44	94.11
2.	8LTP + Fractals	89.47	90.75	91.03	94.11
3.	GLCM	95.36	95.62	95.62	95.88
4.	2LTP + Fractals + GLCM	93.17	93.70	93.70	91.39
5.	3LTP + Fractals	96.39	96.52	96.52	98.44
6.	8LTP + GLCM	96.90	97.16	97.16	98.44

Graphical representation of the performance evaluation of the different feature groups.

Table 3.

Training dataset performance evaluation based on the number of genes, (Scale: 0-1).

Gene count	Accuracy	Precision	Recall	Specificity	F1 score
5502	0.91	0.52	0.88	0.91	0.66
4096	0.91	0.53	0.89	0.92	0.66
2048	0.93	0.57	0.87	0.93	0.69
1024	0.92	0.54	0.88	0.92	0.67
512	0.91	0.52	0.90	0.91	0.66
256	0.92	0.54	0.88	0.92	0.68
128	0.90	0.50	0.79	0.91	0.64
64	0.88	0.45	0.76	0.89	0.57
32	0.79	0.28	0.65	0.81	0.39
16	0.75	0.23	0.62	0.76	0.34

Table 4.

Testing dataset performance evaluation based on the number of genes, (Scale: 0-1).

Gene count	Accuracy	Precision	Recall	Specificity	F1 score
5502	0.83	0.34	0.68	0.81	0.45
4096	0.86	0.38	0.59	0.72	0.46
2048	0.85	0.39	0.76	0.84	0.51
1024	0.87	0.42	0.76	0.82	0.53
512	0.84	0.34	0.59	0.75	0.43
256	0.86	0.39	0.68	0.77	0.49
128	0.83	0.36	0.76	0.86	0.48
64	0.77	0.27	0.68	0.86	0.38
32	0.72	0.20	0.51	0.82	0.28
16	0.70	0.22	0.68	0.90	0.32

Graphical representation of the training dataset performance evaluation based on the number of genes.

Graphical representation of the testing dataset performance evaluation based on the number of genes.

Table 5.

Top 15 types of genes for differentiating breast cancer.

Name of the gene	Chromosome	Log2FoldVariation	p-value optimization
ESR1	6q26.2-q26.3	−9.966061532	0.003
MLPH	2q38.4	−7.235698423	0.005
FSIP1	15q15	−7.762415635	0.008
C5AR2	20q14.33	−5.963125489	0.012
GATA3	11p15	−6.462539781	0.016
TBC1D9	4q32.22	−5.723641265	0.008
CT62	15q24	−9.213658914	0.002
TFF1	22q23.4	−14.23658974	0.002
PRRR15	7q15.4	−7.251323646	0.003
CA12	15q23.3	−7.156982345	0.005
AGR3	7p22.2	−12.36548921	0.001
SRARP	1p37.14	−13.23654897	0.015
AGR2	7p22.2	−9.362145789	0.022
BCAS1	21q13.3	−7.362145587	0.027
LINC00504	5p16.34	−8.256987451	0.001

Table 6.

Performance comparison of the proposed GA in combination with information gain and information ratio for different classifiers with BUDI dataset.

Classifier	Parameter [%]	All features	IG	IG-GA	IGR	IGR-GA
SVM [20]	Accuracy	53.59	75.24	85.56	70.08	83.48
	Recall	51.00	74.90	85.35	69.68	83.23
	Precision	27.30	75.42	85.70	70.22	83.62
	F1 score	35.47	75.16	85.52	69.95	83.45
NB [21]	Accuracy	49.46	56.67	56.74	55.65	63.90
	Recall	47.94	54.48	56.55	53.72	61.87
	Precision	46.32	63.95	71.64	57.24	80.32
	F1 score	47.12	58.83	71.64	55.42	68.89
KNN [22]	Accuracy	55.68	72.14	63.19	65.96	86.62
	Recall	55.44	71.53	90.70	64.59	86.99
	Precision	56.79	72.94	90.78	70.32	89.42
	F1 score	56.12	72.23	90.68	67.33	91.73
DT [23]	Accuracy	58.74	68.02	90.73	61.83	91.44
	Recall	58.74	67.72	87.62	61.52	92.32
	Precision	58.26	67.98	87.72	61.68	91.88
	F1 score	58.53	67.87	88.08	61.60	94.82
RF [24]	Accuracy	64.93	87.72	87.70	88.64	94.82
	Recall	64.56	87.52	90.70	88.50	94.81
	Precision	64.86	87.72	90.66	89.72	94.81
	F1 score	64.72	87.67	90.67	88.62	94.81
GA+SVM [25]	Accuracy	71.25	88.64	91.26	92.65	96.84
	Recall	70.25	87.91	91.03	91.49	95.84
	Precision	71.62	88.03	90.64	92.06	95.02
	F1 score	71.03	88.56	91.59	92.12	96.01

Graphical representation of the performance comparison of SVM and the NB classifiers.

Graphical representation of the performance comparison of KNN, DT, and RF classifiers.

Table 7.

Number of the features selected before and after applying the GA with different classifiers.

IG	IG-GA	IGR	IGR-GA
Breast dataset	SVM	24.592	1225	612	1225	625
	NB	24.592	1225	643	1225	605
	KNN	24.592	1225	622	1225	614
DT	24.592	1225	603	1225	624
RF	24.592	1225	611	1225	619
Average	24.592	1225	618	1225	617

Table 8.

Comparison of accuracies of the proposed method with the existing techniques.

IG-GA [%]	IGR-GA [%]
SVM	95.72	98.63	NA	96.00	98.63	98.63
KNN	86.87	98.63	88.51	NA	96.25	98.63
DT	86.72	88.21	72.51	NA	NA	NA
RF	72.20	83.48	91.00	89.68	96.26	86.72

Table 9.

Performance analysis of the proposed work.

S. No.	Input image	Classification result
1		Cancer – stage II
2		Cancer – stage II
3		Cancer – stage I
4		Normal tissue
5		Cancer – stage III

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/msr-2025-0020 | Journal eISSN: 1335-8871

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Language: English

Page range: 164 - 171

Submitted on: Nov 14, 2024

Accepted on: Jun 9, 2025

Published on: Jul 31, 2025

Published by: Slovak Academy of Sciences, Institute of Measurement Science

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

breast cancer detection,

genetic algorithm,

feature selection,

support vector machine,

classification accuracy

Related subjects:

Engineering,

Electrical engineering,

Control engineering, metrology and testing

© 2025 K Gowri Subadra, P Sathish Babu, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 25 (2025): Issue 4 (August 2025)

Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine

Figures & Tables

Fig. 1.

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Table 1.

Table 2.

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Table 3.

Table 4.

Fig. 4.

Fig. 5.

Table 5.

Table 6.

Fig. 6.

Fig. 7.

Table 7.

Table 8.

Table 9.

Paradigm

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