A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

Charibaldi, N.; Harjoko, A.; Azhari,; Hisyam, B.

A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

International Journal on Smart Sensing and Intelligent Systems

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

By:

N. Charibaldi, A. Harjoko, Azhari, and B. Hisyam

Open Access

|Apr 2018

Figures & Tables

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

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.

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

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

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

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

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%

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

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

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

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%

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

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

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

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

Previous researches_

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%

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

Journal RSS Feed

Language: English

Page range: 1 - 13

Published on: Apr 30, 2018

Published by: Professor Subhas Chandra Mukhopadhyay

In partnership with: Paradigm Publishing Services

Publication frequency: 1 times per year

Keywords:

HGA,

Related subjects:

Introductions and overviews,

Engineering, other

© 2018 N. Charibaldi, A. Harjoko, Azhari,, B. Hisyam, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous article Volume 11 (2018): Issue 1 (January 2018)Next article

A new HGA-FLVQ model for Mycobacterium Tuberculosis detection

Figures & Tables

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

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

Confusion matrix of HGA-FLVQ model using ZN staining method_

Confusion matrix of FLVQ method using ZN staining as Gold standard_

Results of weight calculation and revision of LVQ training_

Performance rates of HGA-FLVQ model_

Confusion matrix of HGA-FLVQ model using ZN staining re-examination_

Results of cluster center revisions of FLVQ training_

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

Final results of HGA-FLVQ model performance_

Results of HGA-FLVQ model training_

Confusion matrix of LVQ method using ZN staining as Gold standard_

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

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

Previous researches_

Paradigm

My account