A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

Reference	Sample type	Classification methods	Results
Fend et al. (2006)	Sputum	Back propagation artificial neural network	Sensitivity 89.09% Specificity 91.14%
Gibson et al. (2009)	Sputum	Linear discriminant analysis	Average Sensitivity 80% Average Specificity 75%
Kolk et al. (2010)	Sputum	Rob electronic-nose: Linear Discriminant Analysis Partial least square discriminant analysis	Sensitivity 57-64% Specificity 61–70% Sensitivity 42–50% Specificity 73–77%
		Walter electronic-nose: Linear discriminant analysis Partial least square discriminant analysis	Sensitivity 56-66% Specificity 65–68% Sensitivity 37–61% Specificity 56–67%

A Cycle of Gas Sensor (TGS) Response in an E-nose Device.

Table 2

Results of HGA-FLVQ model training.

TGS813 Sensor data
Iteration	Fuzziness parameter	Learning rate		Cluster center		Error
1	1.100590	0.000987	0.001400	0.108494	0.024354	0.0014483775
2	1.101180	0.001541	0.000924	0.140709	0.033404	0.00111976
3	1.101770	0.002089	0.000798	0.163612	0.039210	5.5822276E-4
4	1.102360	0.002508	0.000750	0.177564	0.042487	2.0541892E-4
5	1.102950	0.002808	0.000727	0.186139	0.044436	7.73241E-5
TGS822 Sensor data
1	1.100590	0.001144	0.001169	0.093718	0.212932	0.004191581
2	1.101180	0.001008	0.001355	0.097408	0.228077	2.4299692E-4
3	1.101770	0.000919	0.001562	0.102777	0.238711	1.41901E-4
4	1.102360	0.000857	0.001782	0.107080	0.248823	1.207616E-4
5	1.102950	0.000807	0.002042	0.110949	0.259704	1.3338469E-4
6	1.103540	0.000769	0.002332	0.114258	0.270786	1.3375138E-4
7	1.104130	0.000742	0.002620	0.116887	0.280968	1.105932E-4
8	1.104720	0.000723	0.002887	0.118887	0.289624	7.890986E-5
TGS2611 Sensor data
1	1.100590	0.000644	0.005906	0.031570	0.029312	0.0014235964
2	1.101180	0.001916	0.000857	0.055587	0.020714	6.50747E-4
3	1.101770	0.002333	0.000767	0.081313	0.015552	6.8849424E-4
4	1.102360	0.003203	0.000704	0.096225	0.017693	2.2693272E-4
5	1.102950	0.003725	0.000683	0.103321	0.018730	5.1426956E-5

Error Value Decrease in HGA-FLVQ Model Training using TGS813 Sensor Data.

Error Value Decrease in HGA-FLVQ Model Training using TGS822 Sensor Data.

Error Value Decrease in HGA-FLVQ Model Training using TGS2611 Sensor Data.

Table 3

Results of weight calculation and revision of LVQ training.

	TGS813 Sensor	TGS822 Sensor	TGS2611 Sensor
1st Iteration
The weight of the negative class	−0.051673025	0.26946884	−0.003179715
The weight of the positive class	−0.002036426	0.1985224	0.21423542
2nd Iteration
The weight of the negative class	−0.0356808	0.28372976	−3.8830974E-4
The weight of the positive class	0.028204879	0.14191468	0.16531087
3rd Iteration
The weight of the negative class	−0.0029952978	0.3213401	0.00271485
The weight of the positive class	0.052159406	0.10385808	0.13945574
4th Iteration
The weight of the negative class	0.03716613	0.37145796	0.0097912615
The weight of the positive class	0.062625006	0.088705875	0.1361857
5th Iteration
The weight of the negative class	0.07502196	0.407575	0.017326174
The weight of the positive class	0.06537599	0.080875896	0.12186478
6th Iteration
The weight of the negative class	0.09876694	0.42662817	0.02240251
The weight of the positive class	0.06590489	0.07604013	0.1046309
7th Iteration
The weight of the negative class	0.11628124	0.44089812	0.026141549
The weight of the positive class	0.06780671	0.074578255	0.09289357
8th Iteration
The weight of the negative class	0.12786691	0.44939327	0.02856244
The weight of the positive class	0.06953817	0.07365509	0.08497784
9th Iteration
The weight of the negative class	0.13485843	0.45396066	0.029982805
The weight of the positive class	0.07066574	0.07268719	0.08011716
10th Iteration
The weight of the negative class	0.13828559	0.4564701	0.030701837
The weight of the positive class	0.07150341	0.072542615	0.07794089

Table 4

Results of cluster center revisions of FLVQ training.

TGS813 Sensor data
Iteration	Fuzziness parameter	Learning rate		Cluster-Center		Error
1	1.100590	0.001075	0.001401	0.073105	0.072574	0.004639
2	1.101180	0.001309	0.001160	0.079481	0.068403	0.000058
TGS822 Sensor data
1	1.100590	0.001113	0.001346	0.160899	0.142446	0.004211
2	1.101180	0.001361	0.001012	0.218137	0.103790	0.004771
3	1.101770	0.001516	0.000935	0.236398	0.101749	0.000338
4	1.102360	0.001731	0.000869	0.246578	0.106190	0.000123
5	1.102950	0.001982	0.000817	0.257252	0.110136	0.000129
6	1.103540	0.002266	0.000776	0.268349	0.113576	0.000135
7	1.104130	0.002558	0.000747	0.278854	0.116367	0.000118
8	1.104720	0.002829	0.000726	0.287788	0.118478	0.000084
TGS2611 Sensor data
1	1.100590	0.003575	0.000705	0.040732	0.029254	0.001988
2	1.101180	0.002175	0.000788	0.074589	0.016031	0.001321
3	1.101770	0.002993	0.000715	0.092969	0.017238	0.000339
4	1.102360	0.003588	0.000688	0.101591	0.018471	0.000076

Table 5

Testing result of a positive ZN staining patient using HGA-FLVQ model.

Amplitude order	Target class	Class distance 1	Class distance 2	Prediction class
1	2	0.369	0.424	1
2	2	0.357	0.392	1
3	2	0.285	0.238	2
4	2	0.262	0.177	2
5	2	0.337	0.318	2
6	2	0.317	0.150	2
7	2	0.321	0.076	2
8	2	0.310	0.087	2
9	2	0.307	0.090	2
10	2	0.290	0.107	2
11	2	0.286	0.111	2
12	2	0.305	0.092	2
13	2	0.286	0.111	2
14	2	0.284	0.113	2
15	2	0.2880	0.109	2
16	2	0.292	0.105	2
17	2	0.280	0.117	2
18	2	0.288	0.111	2
19	2	0.289	0.112	2
20	2	0.280	0.117	2

Table 6

Confusion matrix of HGA-FLVQ model using ZN staining method.

n = 50	Predicted: No	Predicted: Yes
ZN staining: No	TN = 22	FP = 4	26
ZN staining: Yes	FN = 1	TP = 23	24
	23	27

Table 7

Performance rates of HGA-FLVQ model.

Performance rate	Formula	Result
Accuracy	(TP+TN)/n	(23+22)/50 × 100% = 90.00%
Error rate	(FP+FN)/n	(4+1)/50 × 100% = 10.00%
Sensitivity (true positive rate)	TP/ZN Staining Yes	23/24 × 100% = 95.83%
False positive rate	FP/ZN Staining No	4/26 × 100% = 15.38%
Specificity (true negative rate)	TN/ZN Staining No	22/26 × 100% = 84.62%
Precision	TP/Predictive Yes	23/27 × 100% = 85.19%
Prevalence	TP/n	23/50 ×100% = 46.00%

Table 8

Confusion matrix of HGA-FLVQ model using ZN staining re-examination.

n = 4	Re-Examination ZN Staining: No	Re-Examination ZN Staining: Yes
ZN Staining: No	TN = 1	FP = 3	4
ZN Staining: Yes	FN = 0	TP = 0	0
	1	3

Table 9

Final confusion matrix of HGA-FLVQ model after a re-examination by ZN staining.

n = 50	Predicted: No	Predicted: Yes
ZN Staining: No	TN = 22	FP = 1	23
ZN Staining: Yes	FN = 1	TP = 26	27
	23	27

Table 10

Confusion matrix of LVQ method using ZN staining as Gold standard.

n = 50	Predicted : No	Predicted: Yes
ZN Staining: No	TN=25	FP=1	26
ZN Staining: Yes	FN=3	TP=21	24
	28	22

Table 11

Final confusion matrix of LVQ method after a re-examination by ZN staining.

n = 50	Predicted : No	Predicted: Yes
ZN Staining: No	TN=22	FP=4	26
ZN Staining: Yes	FN=3	TP=21	24
	25	25

Table 12

Confusion matrix of FLVQ method using ZN staining as Gold standard.

n = 50	Predicted : No	Predicted: Yes
ZN Staining: No	TN=25	FP=1	26
ZN Staining: Yes	FN=7	TP=17	24
	32	18

Table 13

Final confusion matrix of FLVQ method after a re-examination by ZN staining.

n = 50	Predicted: No	Predicted: Yes
ZN Staining: No	TN=22	FP=4	26
ZN Staining: Yes	FN=7	TP=17	24
	29	21

Table 14

Final results of HGA-FLVQ model performance.

Performance rates	Formulation	Performance of HGA-FLVQ model	Performance of LVQ method	Performance of FLVQ method
Accuracy	(TP+TN)/n	(26+22)/50 ×100% = 96.00%	(21+25)/50 ×100% = 92.00%	(17+25)/50 ×100% = 84.00%
Error rate	(FP+FN)/n	(1+1)/50 ×100% = 4.00%	(1+3)/50 ×100% = 8.00%	(1+7)/40 ×100% = 16.00%
Sensitivity (true positive rate)	TP/(ZN Staining Yes)	26/27 ×100% = 96.30%	21/24 ×100% = 87.50%	17/24 ×100% = 70.83%
False positive rate	FP/(ZN Staining No)	1/23 ×100% =4.35%	1/26 ×100% = 3.85%	1/26 ×100% = 3.85%
Specificity (true negative rate)	TN/(ZN Staining No)	22/23 ×100% = 95.65%	25/26 ×100% = 96.15%	25/26 ×100% = 96.15%
Precision	TP/Predictive Yes	26/27 ×100% = 96.30%	21/22 ×100% = 95.45%	17/18 ×100% = 94.44%
Prevalence	TP/n	26/50 ×100% = 52.00%	21/50 ×100% = 42.00%	17/50 ×100% = 34.00%

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DOI: https://doi.org/10.21307/ijssis-2018-028 | Journal eISSN: 1178-5608

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Language: English

Page range: 1 - 13

Published on: Apr 30, 2018

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

HGA,

Related subjects:

Introductions and overviews,

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© 2018 N. Charibaldi, A. Harjoko, Azhari, B. Hisyam, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 11 (2018): Issue 1 (January 2018)

A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

Figures & Tables

Table 1

Figure 1

Figure 2