Mobile Diagnosis of Thyroid based on Ensemble Classifier
Year 2020,
Volume: 11 Issue: 3, 915 - 924, 30.09.2020
Ramazan Solmaz
,
Ahmet Alkan
Mücahid Günay
Abstract
The thyroid gland plays a major role in many metabolic activities of the human body. Thyroid disease, which is quite common in humans, affects people's quality of life significantly. Early diagnosis is very important for taking precautions. The mobile diagnostic system can be the solution for early diagnosis especially in rural areas or without going to health institution. This study has been proposed to enable people with mobile devices to obtain quick information about the disease or to seek medical assistance in any matter without going to the hospital. Functional thyroid diagnosis system is designed using mobile device, Android based software application, Database (SQL) and Server (MATLAB based decision algorithms). With the system, functional thyroid disease can be diagnosed using an android based mobile device. Different classification algorithms were searched for the most accurate diagnosis and Ensemble method which has a high success rate for thyroid disease was used in the system. Ensemble classification technique reached a success rate of 99.06% and 99.08% for the first and second data group, respectively. These success rates were calculated by using gold standard test and results were compared with the literature. Obtained test results showed that, the proposed mobile diagnosis system could be used for the diagnosis of the functional thyroid. At the same time, this system can be developed for different diseases.
Supporting Institution
Kahramanmaras Sutcu Imam University
Project Number
2013/4-30M
References
- Electronic Mobile Health
https://www.healthparliament.eu/wpcontent/upload
s/2017/09/Electronic_mobile-health.pdf/19.09.2019
- J. John, & C. Raju, (2018), “Design and Comparative
Analysis of Mobile Computing Software
Framework,” 2018 Second International Conference on
Inventive Communication and Computational
Technologies (ICICCT). doi:10.1109/icicct.2018.8473350.
978-1-5386-1974-2/18/$31.00 ©2018 IEEE.
- A. Uçar, & R. Özalp, (2017), “Efficient android
electronic nose design for recognition and perception
of fruit odors using Kernel Extreme Learning
Machines,” Chemometrics and Intelligent Laboratory
Systems, vol. 166, pp 69–80.
doi:10.1016/j.chemolab.2017.05.013.
- S. A. Tuncer and A. Alkan, "Segmentation of thyroid
nodules with K-means algorithm on mobile devices,"
2015 16th IEEE International Symposium on
Computational Intelligence and Informatics (CINTI),
Budapest, 2015, pp. 345-348.
- S. A. Tuncer and A. Alkan, "Abdominal image
segmentation on Android based mobile devices," 2014
22nd Signal Processing and Communications Applications
Conference (SIU), Trabzon, 2014, pp. 806-809.
- M. F. Amasyali, (2013), “A semi-random subspace
method for classification ensembles,” 21st Signal
Processing and Communications Applications Conference
(SIU). doi:10.1109/siu.2013.6531301. 978-1-4673-5563-
6/13/$31.00 ©2013 IEEE.
- A. Maniakas, L. Davies, & M. E. Zafereo, (2018),
“Thyroid Disease Around the World,” Otolaryngologic
Clinics of North America, vol 51(3), pp 631–642.
doi:10.1016/j.otc.2018.01.014.
- J. Longbottom & R. Macnab, (2014), “Thyroid disease
and thyroid surgery,” Anaesthesia & Intensive Care
Medicine, vol. 15(10), pp 458–464.
doi:10.1016/j.mpaic.2014.07.006
- H. Kaneko, (2018), “Automatic outlier sample
detection based on regression analysis and repeated
ensemble learning,” Chemometrics and Intelligent
Laboratory Systems, vol. 177, pp 74–82.
- M. Hosni, I. Abnane, A. Idri, J. M. C. de Gea, & J. L. F.
Alemán, (2019), “Reviewing Ensemble Classification
Methods in Breast Cancer”, Computer Methods and
Programs in Biomedicine.
doi:10.1016/j.cmpb.2019.05.019.
- U. Agrawal, D. Soria, C. Wagner, J. Garibaldi, I. O.
Ellis, J. M. S. Bartlett, A. R. Green, (2019), “Combining
Clustering and Classification Ensembles: A Novel
Pipeline to Identify Breast Cancer Profiles.” Artificial
Intelligence in Medicine.
doi:10.1016/j.artmed.2019.05.002.
- Y. He, D. Chen, W. Zhao, (2006), “Ensemble classifier
system based on ant colony algorithm and its
application in chemical pattern classification,
Chemometrics and Intelligent Laboratory Systems, vol. 80,
pp. 39 – 49.
- M. Czajkowski & M. Kretowski, (2019), “Decision
Tree Underfitting in Mining of Gene Expression Data
An Evolutionary Multi-Test Tree Approach,” Expert
Systems with Applications.
doi:10.1016/j.eswa.2019.07.019.
- H. Sun, & X. Hu, (2017), “Attribute selection for
decision tree learning with class constraint,”
Chemometrics and Intelligent Laboratory Systems, 163,
16–23. doi:10.1016/j.chemolab.2017.02.004
- N. Cerpa, M. Bardeen, C. A. Astudillo & J. Verner,
(2016), “Evaluating different families of prediction
methods for estimating software project outcomes,”
Journal of Systems and Software, 112, 48–64.
doi:10.1016/j.jss.2015.10.011.
- H. Sun & X. Hu (2017), “Attribute selection for
decision tree learning with class constraint.”
Chemometrics and Intelligent Laboratory Systems, 163,
16–23. doi:10.1016/j.chemolab.2017.02.004.
- Thyroid diseases diagnosis, treatment and follow-up
guide, Turkey Endocrinology and Metabolism
Society, ISBN No: ISBN: 978-605-4011-37-7, 2019.
- https://www.statisticshowto.datasciencecentral.com/
gold-standard-test/Accessed 20/09/2019.
- G. Serpen, H. Jiang, L. Allred, (1997), “Performance
analysis of probabilistic potential function neural
network classifier,” In Proceedings of artificial neural
networks in engineering conference, St. Louis, MO, 7, pp.
471–476.
- L. Ozyilmaz, T. Yildirim, (2002), “Diagnosis of thyroid
disease using artificial neural network methods,” In
Proceedings of ICONIP’02 nineth international conference
on neural information processing, Orchid Country Club,
Singapore, pp. 2033–2036.
- L. Pasi,(2004), “Similarity classifier applied to medical
data sets,” International conference on soft computing,
Helsinki, Finland & Gulf of Finland & Tallinn,
Estonia.
- K. Polat, S. Sahan, S. Gunes, (2007), “A novel hybrid
method based on artificial immune recognition
system (AIRS) with fuzzy weighted pre-processing
for thyroid disease diagnosis,” Expert Systems with
Applications, 32(4), pp. 1141–1147.
- A. Keles, A. Keles, (2008), “ESTDD: expert system for
thyroid diseases diagnosis,” Expert Systems with
Applications, 34(1), pp. 242–246.
- P. Kukkurainen, P. Luukka, (2008), “Classification
method using fuzzy level set subgrouping,” Expert
Systems with Applications, 34, pp. 859–865.
- F. Temurtas, (2009), “A comparative study on thyroid
disease diagnosis using neural networks,” Expert
Systems with Applications, 36(1) pp. 944–949.
- H. Kodaz, S. Ozsen, A. Arslan, S. Gunes, (2009),
“Medical application of information gain based
artificial immune recognition system (AIRS):
diagnosis of thyroid disease,” Expert Systems with
Applications, 36, pp. 3086–3092.
- E. Dogantekin, A. Dogantekin, D. Avci, (2010), “An
automatic diagnosis system based on thyroid gland:
ADSTG,” Expert Systems with Applications, 37, pp.
6368–6372.
- E. Dogantekin, A. Dogantekin, D. Avci, (2011), “An
expert system based on generalized discriminant
analysis and wavelet support vector machine for
diagnosis of thyroid diseases,” Expert Systems with
Applications, 38(1), pp. 146–150.
- H. L. Chen, B. Yang, G. Wang, J. Liu, Y. D. Chen, D. Y.
Liu, (2011), “A three-stage expert system based on
support vector machines for thyroid disease
diagnosis,” J. Med. Syst.,
http://dx.doi.org/10.1007/s10916-011-9655-8.
- D. Y. Liu, H. L. Chen, B. Yang, X. En Lv, L. N. Li
(2011), “Design of an enhanced fuzzy k-nearest
neighbor classifier based computer aided diagnostic
system for thyroid disease,” Journal of Medical Systems,
http://dx.doi.org/10.1007/s10916-011-9815-x.
- L. N. Li, J. H. Ouyang, H. L. Chen, D. Y. Liu, (2012),
“A computer aided diagnosis system for thyroid
disease using extreme learning machine,” J. Med.
Syst., 36, pp. 3327–3337, DOI 10.1007/s10916-012-9825-
3.
- A. Dina, Sharaf-El-Deen, İ. F. Moawad, M. E. Khalifa,
(2014), “A new hybrid case-based reasoning approach
for medical diagnosis systems,” J. Med. Syst. 38(9), Doi
10.1007/s10916-014-0009-1.
- Y. Kaya, (2014) “A Fast-Intelligent Diagnosis System
for “Thyroid Diseases Based on Extreme Learning
Machine,” Journal of Science and Technology A-Applied
Sciences and Engineering, 15(1), pp. 41-49.
https://dergipark.org.tr/en/download/articlefile/35611.
- R. Solmaz, M. Günay, A. Alkan, (2013), “Uzman
Sistemlerin Tiroit Teşhisinde Kullanılması,” Akademik
Bilisim 2013 – XV. Akademik Bilisim Konferansı Bildirileri
23-25 Ocak 2013 – Akdeniz Üniversitesi, Antalya,
Türkiye, pp. 864-867, 2013.
https://ab.org.tr/ab13/bildiri/268.pdf.
- R. Solmaz, A. Alkan, (2013), “Kan Testi Tabanlı
Sınıflandırma Yöntemlerinin Tiroit Tanısında
Kullanılması,” 6. Mühendislik ve Teknoloji Sempozyumu
25-26 Nisan 2013 I Çankaya Üniversitesi, Ankara,
Türkiye, pp. 269-272. ISBN: 978-975-6734-155,
https://zgrw.org/files/MTS6.pd
- R. Solmaz, M. Günay, A. Alkan, (2014), “Fonksiyonel
Tiroit Hastalığı Tanısında Naive Bayes
Sınıflandırıcının Kullanılması,” Akademik Bilişim’14 -
XVI. Akademik Bilişim Konferansı Bildirileri 5 - 7 Şubat
2014 Mersin Üniversitesi, Türkiye, pp. 891-897, 2014.
https://ab.org.tr/ab14/kitap/solmaz_gunay_ab14.pdf.
Mobile diagnosis of thyroid based on ensemble classifier
Year 2020,
Volume: 11 Issue: 3, 915 - 924, 30.09.2020
Ramazan Solmaz
,
Ahmet Alkan
Mücahid Günay
Abstract
The thyroid gland plays a major role in many metabolic activities of the human body. Thyroid disease,
which is quite common in humans, affects people's quality of life significantly. Early diagnosis is very
important for taking precautions. The mobile diagnostic system can be the solution for early diagnosis
especially in rural areas or without going to health institution. This study has been proposed to enable
people with mobile devices to obtain quick information about the disease or to seek medical assistance in
any matter without going to the hospital. Functional thyroid diagnosis system is designed using mobile
device, Android based software application, Database (SQL) and Server (MATLAB based decision
algorithms). With the system, functional thyroid disease can be diagnosed using an android based mobile
device. Different classification algorithms were searched for the most accurate diagnosis and Ensemble
method which has a high success rate for thyroid disease was used in the system. Ensemble classification
technique reached a success rate of 99.06% and 99.08% for the first and second data group, respectively.
These success rates were calculated by using gold standard test and results were compared with the
literature. Obtained test results showed that, the proposed mobile diagnosis system could be used for the
diagnosis of the functional thyroid. At the same time, this system can be developed for different diseases.
Project Number
2013/4-30M
References
- Electronic Mobile Health
https://www.healthparliament.eu/wpcontent/upload
s/2017/09/Electronic_mobile-health.pdf/19.09.2019
- J. John, & C. Raju, (2018), “Design and Comparative
Analysis of Mobile Computing Software
Framework,” 2018 Second International Conference on
Inventive Communication and Computational
Technologies (ICICCT). doi:10.1109/icicct.2018.8473350.
978-1-5386-1974-2/18/$31.00 ©2018 IEEE.
- A. Uçar, & R. Özalp, (2017), “Efficient android
electronic nose design for recognition and perception
of fruit odors using Kernel Extreme Learning
Machines,” Chemometrics and Intelligent Laboratory
Systems, vol. 166, pp 69–80.
doi:10.1016/j.chemolab.2017.05.013.
- S. A. Tuncer and A. Alkan, "Segmentation of thyroid
nodules with K-means algorithm on mobile devices,"
2015 16th IEEE International Symposium on
Computational Intelligence and Informatics (CINTI),
Budapest, 2015, pp. 345-348.
- S. A. Tuncer and A. Alkan, "Abdominal image
segmentation on Android based mobile devices," 2014
22nd Signal Processing and Communications Applications
Conference (SIU), Trabzon, 2014, pp. 806-809.
- M. F. Amasyali, (2013), “A semi-random subspace
method for classification ensembles,” 21st Signal
Processing and Communications Applications Conference
(SIU). doi:10.1109/siu.2013.6531301. 978-1-4673-5563-
6/13/$31.00 ©2013 IEEE.
- A. Maniakas, L. Davies, & M. E. Zafereo, (2018),
“Thyroid Disease Around the World,” Otolaryngologic
Clinics of North America, vol 51(3), pp 631–642.
doi:10.1016/j.otc.2018.01.014.
- J. Longbottom & R. Macnab, (2014), “Thyroid disease
and thyroid surgery,” Anaesthesia & Intensive Care
Medicine, vol. 15(10), pp 458–464.
doi:10.1016/j.mpaic.2014.07.006
- H. Kaneko, (2018), “Automatic outlier sample
detection based on regression analysis and repeated
ensemble learning,” Chemometrics and Intelligent
Laboratory Systems, vol. 177, pp 74–82.
- M. Hosni, I. Abnane, A. Idri, J. M. C. de Gea, & J. L. F.
Alemán, (2019), “Reviewing Ensemble Classification
Methods in Breast Cancer”, Computer Methods and
Programs in Biomedicine.
doi:10.1016/j.cmpb.2019.05.019.
- U. Agrawal, D. Soria, C. Wagner, J. Garibaldi, I. O.
Ellis, J. M. S. Bartlett, A. R. Green, (2019), “Combining
Clustering and Classification Ensembles: A Novel
Pipeline to Identify Breast Cancer Profiles.” Artificial
Intelligence in Medicine.
doi:10.1016/j.artmed.2019.05.002.
- Y. He, D. Chen, W. Zhao, (2006), “Ensemble classifier
system based on ant colony algorithm and its
application in chemical pattern classification,
Chemometrics and Intelligent Laboratory Systems, vol. 80,
pp. 39 – 49.
- M. Czajkowski & M. Kretowski, (2019), “Decision
Tree Underfitting in Mining of Gene Expression Data
An Evolutionary Multi-Test Tree Approach,” Expert
Systems with Applications.
doi:10.1016/j.eswa.2019.07.019.
- H. Sun, & X. Hu, (2017), “Attribute selection for
decision tree learning with class constraint,”
Chemometrics and Intelligent Laboratory Systems, 163,
16–23. doi:10.1016/j.chemolab.2017.02.004
- N. Cerpa, M. Bardeen, C. A. Astudillo & J. Verner,
(2016), “Evaluating different families of prediction
methods for estimating software project outcomes,”
Journal of Systems and Software, 112, 48–64.
doi:10.1016/j.jss.2015.10.011.
- H. Sun & X. Hu (2017), “Attribute selection for
decision tree learning with class constraint.”
Chemometrics and Intelligent Laboratory Systems, 163,
16–23. doi:10.1016/j.chemolab.2017.02.004.
- Thyroid diseases diagnosis, treatment and follow-up
guide, Turkey Endocrinology and Metabolism
Society, ISBN No: ISBN: 978-605-4011-37-7, 2019.
- https://www.statisticshowto.datasciencecentral.com/
gold-standard-test/Accessed 20/09/2019.
- G. Serpen, H. Jiang, L. Allred, (1997), “Performance
analysis of probabilistic potential function neural
network classifier,” In Proceedings of artificial neural
networks in engineering conference, St. Louis, MO, 7, pp.
471–476.
- L. Ozyilmaz, T. Yildirim, (2002), “Diagnosis of thyroid
disease using artificial neural network methods,” In
Proceedings of ICONIP’02 nineth international conference
on neural information processing, Orchid Country Club,
Singapore, pp. 2033–2036.
- L. Pasi,(2004), “Similarity classifier applied to medical
data sets,” International conference on soft computing,
Helsinki, Finland & Gulf of Finland & Tallinn,
Estonia.
- K. Polat, S. Sahan, S. Gunes, (2007), “A novel hybrid
method based on artificial immune recognition
system (AIRS) with fuzzy weighted pre-processing
for thyroid disease diagnosis,” Expert Systems with
Applications, 32(4), pp. 1141–1147.
- A. Keles, A. Keles, (2008), “ESTDD: expert system for
thyroid diseases diagnosis,” Expert Systems with
Applications, 34(1), pp. 242–246.
- P. Kukkurainen, P. Luukka, (2008), “Classification
method using fuzzy level set subgrouping,” Expert
Systems with Applications, 34, pp. 859–865.
- F. Temurtas, (2009), “A comparative study on thyroid
disease diagnosis using neural networks,” Expert
Systems with Applications, 36(1) pp. 944–949.
- H. Kodaz, S. Ozsen, A. Arslan, S. Gunes, (2009),
“Medical application of information gain based
artificial immune recognition system (AIRS):
diagnosis of thyroid disease,” Expert Systems with
Applications, 36, pp. 3086–3092.
- E. Dogantekin, A. Dogantekin, D. Avci, (2010), “An
automatic diagnosis system based on thyroid gland:
ADSTG,” Expert Systems with Applications, 37, pp.
6368–6372.
- E. Dogantekin, A. Dogantekin, D. Avci, (2011), “An
expert system based on generalized discriminant
analysis and wavelet support vector machine for
diagnosis of thyroid diseases,” Expert Systems with
Applications, 38(1), pp. 146–150.
- H. L. Chen, B. Yang, G. Wang, J. Liu, Y. D. Chen, D. Y.
Liu, (2011), “A three-stage expert system based on
support vector machines for thyroid disease
diagnosis,” J. Med. Syst.,
http://dx.doi.org/10.1007/s10916-011-9655-8.
- D. Y. Liu, H. L. Chen, B. Yang, X. En Lv, L. N. Li
(2011), “Design of an enhanced fuzzy k-nearest
neighbor classifier based computer aided diagnostic
system for thyroid disease,” Journal of Medical Systems,
http://dx.doi.org/10.1007/s10916-011-9815-x.
- L. N. Li, J. H. Ouyang, H. L. Chen, D. Y. Liu, (2012),
“A computer aided diagnosis system for thyroid
disease using extreme learning machine,” J. Med.
Syst., 36, pp. 3327–3337, DOI 10.1007/s10916-012-9825-
3.
- A. Dina, Sharaf-El-Deen, İ. F. Moawad, M. E. Khalifa,
(2014), “A new hybrid case-based reasoning approach
for medical diagnosis systems,” J. Med. Syst. 38(9), Doi
10.1007/s10916-014-0009-1.
- Y. Kaya, (2014) “A Fast-Intelligent Diagnosis System
for “Thyroid Diseases Based on Extreme Learning
Machine,” Journal of Science and Technology A-Applied
Sciences and Engineering, 15(1), pp. 41-49.
https://dergipark.org.tr/en/download/articlefile/35611.
- R. Solmaz, M. Günay, A. Alkan, (2013), “Uzman
Sistemlerin Tiroit Teşhisinde Kullanılması,” Akademik
Bilisim 2013 – XV. Akademik Bilisim Konferansı Bildirileri
23-25 Ocak 2013 – Akdeniz Üniversitesi, Antalya,
Türkiye, pp. 864-867, 2013.
https://ab.org.tr/ab13/bildiri/268.pdf.
- R. Solmaz, A. Alkan, (2013), “Kan Testi Tabanlı
Sınıflandırma Yöntemlerinin Tiroit Tanısında
Kullanılması,” 6. Mühendislik ve Teknoloji Sempozyumu
25-26 Nisan 2013 I Çankaya Üniversitesi, Ankara,
Türkiye, pp. 269-272. ISBN: 978-975-6734-155,
https://zgrw.org/files/MTS6.pd
- R. Solmaz, M. Günay, A. Alkan, (2014), “Fonksiyonel
Tiroit Hastalığı Tanısında Naive Bayes
Sınıflandırıcının Kullanılması,” Akademik Bilişim’14 -
XVI. Akademik Bilişim Konferansı Bildirileri 5 - 7 Şubat
2014 Mersin Üniversitesi, Türkiye, pp. 891-897, 2014.
https://ab.org.tr/ab14/kitap/solmaz_gunay_ab14.pdf.