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A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Yıl 2020, Cilt: 3 Sayı: 3, 366 - 375, 30.12.2020

Bihter Daş

https://doi.org/10.35377/saucis.03.03.815556
Cited By: 3

Öz

In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.

Anahtar Kelimeler

Liver diseases early diagnosis data classifiers data mining

Kaynakça

  • D. Zakim, & T.D. Boyer, “Hepatology: A Textbook of Liver Disease” (4th ed.). Saunders; 4 edition (August 19, 2002), ISBN 9780721690513.
  • G.J.Tortora, & B.H. Derrickson, “Principles of Anatomy and Physiology” (12th ed.). John Wiley & Sons. p. 945. 2008, ISBN 978-0-470-08471-7.
  • L.M. Friedman, & E. B. Keeffe, “Handbook of Liver Disease”, 3rd Edition, 2012, ISBN 9781437717259.
  • A. Yahiaoui, O. Er, ve N. Yumusak, “A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines”, Biomedical research, vol.28, no.9, 2017.
  • H. Temurtas, N. Yumusak, ve F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks”, Expert Systems with Applications, vol.36, no.4, pp.8610-8615, May. 2009, doi: 10.1016/j.eswa.2008.10.032.
  • O. Er, N. Yumusak, ve F. Temurtas, “Chest diseases diagnosis using artificial neural networks”, Expert Systems with Applications, c. 37, sy 12, ss. 7648-7655, 2010, doi: 10.1016/j.eswa.2010.04.078.
  • R. Das, A. Sengur, “Evaluation of ensemble methods for diagnosing of valvular heart disease”, Expert Systems with Applications, 37(7), 5110-5115, 2010.
  • Z. Karapinar Senturk, “Early diagnosis of Parkinson’s disease using machine learning algorithms”, Medical Hypotheses 138, 109603, 2020.
  • S. Gupta, D. Kumar, & A. Sharma, “Performance analysis of various data mining classification techniques on healthcare data”, International journal of computer science & Information Technology (IJCSIT), 3(4), 155-169, 2011.
  • S.N.N. Alfisahrin, & T. Mantoro, “Data Mining Techniques for Optimization of Liver Disease Classification”, IEEE International Conference in Advanced Computer Science Applications and Technologies (ACSAT), (pp. 379-384), 2013.
  • S. Bahramirad, A. Mustapha, & M. Eshraghi, “Classification of liver disease diagnosis: a comparative study”, In Informatics and Applications (ICIA), 2013 Second International Conference on (pp. 42-46), 2013 IEEE.
  • Y.S. Kim, S.Y. Sohn, & C.N. Yoon, “Screening test data analysis for liver disease prediction model using growth curve”, Biomedicine & pharmacotherapy, 57(10), 482-488, 2003.
  • S. Karthik, A. Priyadarishini, J. Anuradha, & B.K. Tripathy, “Classification and rule extraction using rough set for diagnosis of liver disease and its types”, Advanced Applied Sci. Res, 2(3), 334-345, 2011.
  • A. Gulia, R. Vohra, & P.Rani, “Liver patient classification using intelligent techniques”. International Journal of Computer Science and Information Technologies, 5(4), 5110-5115, 2014.
  • C. Liang, & L. Peng, “An automated diagnosis system of liver disease using artificial immune and genetic algorithms”, Journal of medical systems, 37(2), 9932, 2013.
  • UCI, ILPD (Indian Liver Patient Dataset, 2018), [Online]. Available: https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset), [Accessed: May 26, 2020].
  • Bashir, S., Qamar, U., & Khan, F. H. “IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework”, Journal of biomedical informatics, 59, 185-200, 2016.
  • M. Abdar, “A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms”, Cumhuriyet Science Journal, 36(3), 3230-3241, 2015.
  • J.Pérez, E. Iturbide, V.Olivares, M. Hidalgo, A. Martínez & N. Almanza, “A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases”, Journal of Medical Systems, 39(11), 152, 2015.
  • U. R. Acharya, H. Fujita, S. Bhat, U. Raghavendra, A. Gudigar, F. Molinari & K.H.Ng, “Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images”. Information Fusion, 29, 32-39, 2016.
  • L. Saba, N.Dey, A.S. Ashour, S. Samanta, S.S. Nath, S.Chakraborty & J.S. Suri, “Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm”, Computer methods and programs in biomedicine, 130, 118-134, 2016.
  • T.R. Baitharu, & S.K. Pani, “Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset”, Procedia Computer Science, 85, 862-870, 2016.
  • M. Abdar, M. Zomorodi-Moghadam, R. Das & I.H. Ting, “Performance analysis of classification algorithms on early detection of liver disease”, Expert Systems with Applications, 67, 239-251, 2017.
  • M. Abdar, N.Y. Yen, C.H. Jason, (2017). “Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees”, Journal of Medical and Biological Engineering, 38, pp. 953–965, 2018.
  • R. Das, I. Turkoglu, A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles”, Expert Systems with Applications, vol. 36 no.4, pp. 7675-7680, May. 2009, doi: 10.1016/j.eswa.2008.09.013.
  • R. Das, “A comparison of multiple classification methods for diagnosis of Parkinson disease,” Expert Systems with Applications, vol. 37, no. 2, pp. 1568–1572, Mar. 2010, doi: 10.1016/j.eswa.2009.06.040.
  • SAS Product Documentation, 2020. [Online]. Available: http://support.sas.com/documentation, [Accessed, 01 June, 2020].
  • R. Das and I. Turkoglu, “Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method,” Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 6635–6644, Apr. 2009, doi: 10.1016/j.eswa.2008.08.067.
  • B. V.Ramana, M. S. Prasad Babu and N. B. Venkateswarlu, “Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis”, International Journal of Computer Science Issues, ISSN :1694-0784, May 2012.
  • D. Dua and C. Graff, UCI Machine Learning Repository, 2019. [Online]. Available: [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, [Accessed, 01 Jan, 2020].

Yıl 2020, Cilt: 3 Sayı: 3, 366 - 375, 30.12.2020

Bihter Daş

https://doi.org/10.35377/saucis.03.03.815556
Cited By: 3

Öz

Kaynakça

  • D. Zakim, & T.D. Boyer, “Hepatology: A Textbook of Liver Disease” (4th ed.). Saunders; 4 edition (August 19, 2002), ISBN 9780721690513.
  • G.J.Tortora, & B.H. Derrickson, “Principles of Anatomy and Physiology” (12th ed.). John Wiley & Sons. p. 945. 2008, ISBN 978-0-470-08471-7.
  • L.M. Friedman, & E. B. Keeffe, “Handbook of Liver Disease”, 3rd Edition, 2012, ISBN 9781437717259.
  • A. Yahiaoui, O. Er, ve N. Yumusak, “A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines”, Biomedical research, vol.28, no.9, 2017.
  • H. Temurtas, N. Yumusak, ve F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks”, Expert Systems with Applications, vol.36, no.4, pp.8610-8615, May. 2009, doi: 10.1016/j.eswa.2008.10.032.
  • O. Er, N. Yumusak, ve F. Temurtas, “Chest diseases diagnosis using artificial neural networks”, Expert Systems with Applications, c. 37, sy 12, ss. 7648-7655, 2010, doi: 10.1016/j.eswa.2010.04.078.
  • R. Das, A. Sengur, “Evaluation of ensemble methods for diagnosing of valvular heart disease”, Expert Systems with Applications, 37(7), 5110-5115, 2010.
  • Z. Karapinar Senturk, “Early diagnosis of Parkinson’s disease using machine learning algorithms”, Medical Hypotheses 138, 109603, 2020.
  • S. Gupta, D. Kumar, & A. Sharma, “Performance analysis of various data mining classification techniques on healthcare data”, International journal of computer science & Information Technology (IJCSIT), 3(4), 155-169, 2011.
  • S.N.N. Alfisahrin, & T. Mantoro, “Data Mining Techniques for Optimization of Liver Disease Classification”, IEEE International Conference in Advanced Computer Science Applications and Technologies (ACSAT), (pp. 379-384), 2013.
  • S. Bahramirad, A. Mustapha, & M. Eshraghi, “Classification of liver disease diagnosis: a comparative study”, In Informatics and Applications (ICIA), 2013 Second International Conference on (pp. 42-46), 2013 IEEE.
  • Y.S. Kim, S.Y. Sohn, & C.N. Yoon, “Screening test data analysis for liver disease prediction model using growth curve”, Biomedicine & pharmacotherapy, 57(10), 482-488, 2003.
  • S. Karthik, A. Priyadarishini, J. Anuradha, & B.K. Tripathy, “Classification and rule extraction using rough set for diagnosis of liver disease and its types”, Advanced Applied Sci. Res, 2(3), 334-345, 2011.
  • A. Gulia, R. Vohra, & P.Rani, “Liver patient classification using intelligent techniques”. International Journal of Computer Science and Information Technologies, 5(4), 5110-5115, 2014.
  • C. Liang, & L. Peng, “An automated diagnosis system of liver disease using artificial immune and genetic algorithms”, Journal of medical systems, 37(2), 9932, 2013.
  • UCI, ILPD (Indian Liver Patient Dataset, 2018), [Online]. Available: https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset), [Accessed: May 26, 2020].
  • Bashir, S., Qamar, U., & Khan, F. H. “IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework”, Journal of biomedical informatics, 59, 185-200, 2016.
  • M. Abdar, “A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms”, Cumhuriyet Science Journal, 36(3), 3230-3241, 2015.
  • J.Pérez, E. Iturbide, V.Olivares, M. Hidalgo, A. Martínez & N. Almanza, “A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases”, Journal of Medical Systems, 39(11), 152, 2015.
  • U. R. Acharya, H. Fujita, S. Bhat, U. Raghavendra, A. Gudigar, F. Molinari & K.H.Ng, “Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images”. Information Fusion, 29, 32-39, 2016.
  • L. Saba, N.Dey, A.S. Ashour, S. Samanta, S.S. Nath, S.Chakraborty & J.S. Suri, “Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm”, Computer methods and programs in biomedicine, 130, 118-134, 2016.
  • T.R. Baitharu, & S.K. Pani, “Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset”, Procedia Computer Science, 85, 862-870, 2016.
  • M. Abdar, M. Zomorodi-Moghadam, R. Das & I.H. Ting, “Performance analysis of classification algorithms on early detection of liver disease”, Expert Systems with Applications, 67, 239-251, 2017.
  • M. Abdar, N.Y. Yen, C.H. Jason, (2017). “Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees”, Journal of Medical and Biological Engineering, 38, pp. 953–965, 2018.
  • R. Das, I. Turkoglu, A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles”, Expert Systems with Applications, vol. 36 no.4, pp. 7675-7680, May. 2009, doi: 10.1016/j.eswa.2008.09.013.
  • R. Das, “A comparison of multiple classification methods for diagnosis of Parkinson disease,” Expert Systems with Applications, vol. 37, no. 2, pp. 1568–1572, Mar. 2010, doi: 10.1016/j.eswa.2009.06.040.
  • SAS Product Documentation, 2020. [Online]. Available: http://support.sas.com/documentation, [Accessed, 01 June, 2020].
  • R. Das and I. Turkoglu, “Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method,” Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 6635–6644, Apr. 2009, doi: 10.1016/j.eswa.2008.08.067.
  • B. V.Ramana, M. S. Prasad Babu and N. B. Venkateswarlu, “Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis”, International Journal of Computer Science Issues, ISSN :1694-0784, May 2012.
  • D. Dua and C. Graff, UCI Machine Learning Repository, 2019. [Online]. Available: [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, [Accessed, 01 Jan, 2020].
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Mühendisliği
Bölüm Makaleler
Yazarlar

Bihter Daş 0000-0002-2498-3297

Yayımlanma Tarihi 30 Aralık 2020
Gönderilme Tarihi 24 Ekim 2020
Kabul Tarihi 18 Aralık 2020
Yayımlandığı Sayı Yıl 2020Cilt: 3 Sayı: 3

Kaynak Göster

IEEE B. Daş, “A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases”, SAUCIS, c. 3, sy. 3, ss. 366–375, 2020, doi: 10.35377/saucis.03.03.815556.

Cited By

A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance
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An ensembling approach to predict hepatitis in patients with liver disease using machine learning
VFAST Transactions on Software Engineering
https://doi.org/10.21015/vtse.v11i3.1598
Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals
Cognitive Neurodynamics
https://doi.org/10.1007/s11571-023-10010-y
 Kapak Resmi İndir

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