Clinical Decision Support for Disease Classification based on the Tests Association
Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
[1] Abdullah U, Ahmad J, Ahmed A (2008) Analysis of effectiveness of
apriori algorithm in medical billing data mining, in Proceedings of 4th
International Conference on Emerging Technologies.
[2] Bassan R, Scofano M, Gamarski R, Dohmann HF, Pimenta L, Volschan
A, Araujo M, Clare C, Fabricio M, Sanmartin CH, Mohallem K, Gaspar
S, Macaciel R (2000) Chest pain in the emergency room: Importance of a
systematic approach. Arquivos Brasileiros de Cardiologia 74(1):13-29.
[3] Butler KH, Swencki SA (2006) Chest pain: a clinical assessment.
Radiologic Clinics of North America 44(2):165-179.
[4] Ceglowski R, Churilov L, Wasserthiel J (2007) Combining data mining
and discrete event simulation for a value-added view of a hospital
emergency department. Journal of the Operational Research Society
58:246-254.
[5] Cios KJ, Moore GW (2002) Uniqueness of medical data mining.
Artificial intelligence in medicine 26(1-2):1-24.
[6] Conforti D, Guido R (2005) Kernel-based support vector machine
classifiers for early detection of myocardial infarction. Optimization
Methods and Software 20(2-3):401-413.
[7] Conroy RM et al. (2003) Estimation of ten-year risk of fatal
cardiovascular disease in Europe: the SCORE project. European Heart
Journal 24(11):987-1003.
[8] Duguay C, Chetouane F (2007) Modeling and improving emergency
department systems using discrete event simulation. Simulation
834):311-320.
[9] Ellenius J, Groth T (2000) Transferability of neural network-based
decision support algorithms for early assessment of chest-pain patients.
International Journal of Medical Informatics 60(1):1-20.
[10] Erhardt L, Herlitz J, Bossaert L, Halinen M, Keltari M, Koster R,
Marcassa C, Quinn T, van Weert H (2002) Task force on the management
of chest pain. European Heat Journal 23:1153-1176.
[11] Fayyad UM (1997) Data mining and knowledge discovery in databases:
implications for scientific databases, in Proceedings of Ninth
International Conference on Scientific and Statistical Database
Management.
[12] Fromm RE, Gibbs LR, McCallum WG, Niziol C, Babcock JC, Gueler
AC, Levine RL (1993) Critical care in the emergency department: a
time-based study. Critical Care Medicine 21(7):970-976.
[13] Giudici P (2003) Applied Data Mining: Statistical Methods for Business
and Industry. John Wiley & Sons, Hoboken.
[14] Guven A, Kara S (2006) Classification of electro-oculogram signals using
artificial neural network. Expert Systems with Applications
31(1):199-205.
[15] Jegelevicius D, Lukosevicius A, Paunksnis A, Barzdziukas V (2002)
Application of data mining technique for diagnosis of posterior Uveal
Melanoma. Informatica 13(4):455-464.
[16] Kannel WB, Vasan RS (2009) Is age really a non-modifiable
cardiovascular risk factor. American Journal of Cardiology
104(9):1307-1310.
[17] Kantardzic MM, Zurada J (2005) Next Generation of Data-Mining
Applications. John Wiley & Sons, Hoboken.
[18] Kenneth HB, Sharon AS (2006) Chest pain: A clinical assessment.
Radiologic Clinics of North America 44:165-179.
[19] Khan FS, Anwer RM, Torgersson O, Falkman G (2008) Data mining in
oral medicine using decision trees. World Academy of Science,
Engineering and Technology 37:225-230.
[20] Kononenko I (2001) Machine learning for medical diagnosis: history,
state of the art and perspective. Artificial Intelligence in Medicine
23(1):89-109.
[21] Lin WT, Wang ST, Chiang TC, Shi YX, Chen WY, Chen HM (2010)
Abnormal diagnosis of emergency department triage explored with data
mining technology: An emergency department at a medical center in
Taiwan taken as an example. Expert Systems with Applications
37(4):2733-2741.
[22] Majumder SK, Ghosh N, Gupta PK (2005) Support vector machine for
optical diagnosis of cancer. Journal of Biomedical Optics 10(2):24-34.
[23] Martinez-Selles M, Bueno H, Sacrist├ín A, Estévez A, Ortiz J, Gallego L,
Fern├índez-Avilés F (2008) Chest pain in the emergency department:
incidence, clinical characteristics and risk stratification. Revista Espanola
de Cardiologia 61(9):953-959.
[24] Masuda G, Sakamoto N, Yamamoto R (2002) A framework for dynamic
evidence based medicine using data mining, in Proceedings of 15th IEEE
Symposium on Computer-Based Medical Systems.
[25] Mitchell TM (1999) Machine Learning and Data Mining.
Communications of the ACM 42(11):30-36.
[26] Motoda H, Ohara K (2009) Apriori. in: V. Kumar (Eds.), The Top Ten
Algorithms in Data Mining. Chapman & Hall/CRC, FL, 61-92.
[27] Qiao DD (2009) Clinical diagnosis of life threatening chest diseases:
based on 156 cases. Journal of Chinese Modern Medicine.
[28] Quinlan JR (1993) C4.5: programs for machine learning. Morgan
Kaufmann Publishers.
[29] Ramakrishnan N (2009) C4.5. in: V. Kumar (Eds.), The Top Ten
Algorithms in Data Mining. Chapman & Hall/CRC, FL, 1-19.
[30] Ren H (2007) Clinical diagnosis of chest pain. Chinese Journal for
Clinicians 36:5-7.
[31] Riccardo B, Blaz Z (2008) Predictive data mining in clinical medicine:
current issues and guidelines. International Journal of Medical
Informatics 77:81-97.
[32] Ridker PM, Hennekens CH, Buring JE, Rifai N (2000) C-reactive protein
and other markers of inflammation in the prediction of cardiovascular
disease in women. New England Journal of Medicine 342:836-843.
[33] Rossille D, Cuggia M, Arnault A, Bouget J, Le Beux P (2008) Managing
an emergency department by analyzing HIS medical data: a focus on
elderly patient clinical pathways. Health Care Management Science
11(2):139-146.
[34] Shmueli G, Patel NR, Bruce PC (2007) Data Mining for Business
Intelligence: Concepts, Techniques, and Applications in Microsoft Office
Excel with XLMiner. John Wiley & Sons, Hoboken.
[35] Statistics Korea (2010), http://www.kostat.go.kr/.
[36] Tan Y, Yin GF, Li GB, Chen JY (2007) Mining compatibility rules from
irregular Chinese traditional medicine database by Apriori algorithm.
Journal of Southwest JiaoTong University 15(4).
[37] Velagaleti RS, Massaro J, Vasan RS, Robins SJ, Kannel WB, Levy D
(2009) Relations of lipid concentrations to heart failure incidence: the
Framingham Heart Study. Circulation 120(23):2345-2351.
[38] Yun YP (2008) Application and research of data mining based on C4.5
Algorithm. Harbin University of Science and Technology.
[39] Zaki MJ (2004) Mining non-redundant association rules. Data Mining
and Knowledge Discovery 9(3):223-248.
[1] Abdullah U, Ahmad J, Ahmed A (2008) Analysis of effectiveness of
apriori algorithm in medical billing data mining, in Proceedings of 4th
International Conference on Emerging Technologies.
[2] Bassan R, Scofano M, Gamarski R, Dohmann HF, Pimenta L, Volschan
A, Araujo M, Clare C, Fabricio M, Sanmartin CH, Mohallem K, Gaspar
S, Macaciel R (2000) Chest pain in the emergency room: Importance of a
systematic approach. Arquivos Brasileiros de Cardiologia 74(1):13-29.
[3] Butler KH, Swencki SA (2006) Chest pain: a clinical assessment.
Radiologic Clinics of North America 44(2):165-179.
[4] Ceglowski R, Churilov L, Wasserthiel J (2007) Combining data mining
and discrete event simulation for a value-added view of a hospital
emergency department. Journal of the Operational Research Society
58:246-254.
[5] Cios KJ, Moore GW (2002) Uniqueness of medical data mining.
Artificial intelligence in medicine 26(1-2):1-24.
[6] Conforti D, Guido R (2005) Kernel-based support vector machine
classifiers for early detection of myocardial infarction. Optimization
Methods and Software 20(2-3):401-413.
[7] Conroy RM et al. (2003) Estimation of ten-year risk of fatal
cardiovascular disease in Europe: the SCORE project. European Heart
Journal 24(11):987-1003.
[8] Duguay C, Chetouane F (2007) Modeling and improving emergency
department systems using discrete event simulation. Simulation
834):311-320.
[9] Ellenius J, Groth T (2000) Transferability of neural network-based
decision support algorithms for early assessment of chest-pain patients.
International Journal of Medical Informatics 60(1):1-20.
[10] Erhardt L, Herlitz J, Bossaert L, Halinen M, Keltari M, Koster R,
Marcassa C, Quinn T, van Weert H (2002) Task force on the management
of chest pain. European Heat Journal 23:1153-1176.
[11] Fayyad UM (1997) Data mining and knowledge discovery in databases:
implications for scientific databases, in Proceedings of Ninth
International Conference on Scientific and Statistical Database
Management.
[12] Fromm RE, Gibbs LR, McCallum WG, Niziol C, Babcock JC, Gueler
AC, Levine RL (1993) Critical care in the emergency department: a
time-based study. Critical Care Medicine 21(7):970-976.
[13] Giudici P (2003) Applied Data Mining: Statistical Methods for Business
and Industry. John Wiley & Sons, Hoboken.
[14] Guven A, Kara S (2006) Classification of electro-oculogram signals using
artificial neural network. Expert Systems with Applications
31(1):199-205.
[15] Jegelevicius D, Lukosevicius A, Paunksnis A, Barzdziukas V (2002)
Application of data mining technique for diagnosis of posterior Uveal
Melanoma. Informatica 13(4):455-464.
[16] Kannel WB, Vasan RS (2009) Is age really a non-modifiable
cardiovascular risk factor. American Journal of Cardiology
104(9):1307-1310.
[17] Kantardzic MM, Zurada J (2005) Next Generation of Data-Mining
Applications. John Wiley & Sons, Hoboken.
[18] Kenneth HB, Sharon AS (2006) Chest pain: A clinical assessment.
Radiologic Clinics of North America 44:165-179.
[19] Khan FS, Anwer RM, Torgersson O, Falkman G (2008) Data mining in
oral medicine using decision trees. World Academy of Science,
Engineering and Technology 37:225-230.
[20] Kononenko I (2001) Machine learning for medical diagnosis: history,
state of the art and perspective. Artificial Intelligence in Medicine
23(1):89-109.
[21] Lin WT, Wang ST, Chiang TC, Shi YX, Chen WY, Chen HM (2010)
Abnormal diagnosis of emergency department triage explored with data
mining technology: An emergency department at a medical center in
Taiwan taken as an example. Expert Systems with Applications
37(4):2733-2741.
[22] Majumder SK, Ghosh N, Gupta PK (2005) Support vector machine for
optical diagnosis of cancer. Journal of Biomedical Optics 10(2):24-34.
[23] Martinez-Selles M, Bueno H, Sacrist├ín A, Estévez A, Ortiz J, Gallego L,
Fern├índez-Avilés F (2008) Chest pain in the emergency department:
incidence, clinical characteristics and risk stratification. Revista Espanola
de Cardiologia 61(9):953-959.
[24] Masuda G, Sakamoto N, Yamamoto R (2002) A framework for dynamic
evidence based medicine using data mining, in Proceedings of 15th IEEE
Symposium on Computer-Based Medical Systems.
[25] Mitchell TM (1999) Machine Learning and Data Mining.
Communications of the ACM 42(11):30-36.
[26] Motoda H, Ohara K (2009) Apriori. in: V. Kumar (Eds.), The Top Ten
Algorithms in Data Mining. Chapman & Hall/CRC, FL, 61-92.
[27] Qiao DD (2009) Clinical diagnosis of life threatening chest diseases:
based on 156 cases. Journal of Chinese Modern Medicine.
[28] Quinlan JR (1993) C4.5: programs for machine learning. Morgan
Kaufmann Publishers.
[29] Ramakrishnan N (2009) C4.5. in: V. Kumar (Eds.), The Top Ten
Algorithms in Data Mining. Chapman & Hall/CRC, FL, 1-19.
[30] Ren H (2007) Clinical diagnosis of chest pain. Chinese Journal for
Clinicians 36:5-7.
[31] Riccardo B, Blaz Z (2008) Predictive data mining in clinical medicine:
current issues and guidelines. International Journal of Medical
Informatics 77:81-97.
[32] Ridker PM, Hennekens CH, Buring JE, Rifai N (2000) C-reactive protein
and other markers of inflammation in the prediction of cardiovascular
disease in women. New England Journal of Medicine 342:836-843.
[33] Rossille D, Cuggia M, Arnault A, Bouget J, Le Beux P (2008) Managing
an emergency department by analyzing HIS medical data: a focus on
elderly patient clinical pathways. Health Care Management Science
11(2):139-146.
[34] Shmueli G, Patel NR, Bruce PC (2007) Data Mining for Business
Intelligence: Concepts, Techniques, and Applications in Microsoft Office
Excel with XLMiner. John Wiley & Sons, Hoboken.
[35] Statistics Korea (2010), http://www.kostat.go.kr/.
[36] Tan Y, Yin GF, Li GB, Chen JY (2007) Mining compatibility rules from
irregular Chinese traditional medicine database by Apriori algorithm.
Journal of Southwest JiaoTong University 15(4).
[37] Velagaleti RS, Massaro J, Vasan RS, Robins SJ, Kannel WB, Levy D
(2009) Relations of lipid concentrations to heart failure incidence: the
Framingham Heart Study. Circulation 120(23):2345-2351.
[38] Yun YP (2008) Application and research of data mining based on C4.5
Algorithm. Harbin University of Science and Technology.
[39] Zaki MJ (2004) Mining non-redundant association rules. Data Mining
and Knowledge Discovery 9(3):223-248.
@article{"International Journal of Medical, Medicine and Health Sciences:63442", author = "Sung Ho Ha and Seong Hyeon Joo and Eun Kyung Kwon", title = "Clinical Decision Support for Disease Classification based on the Tests Association", abstract = "Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.", keywords = "Diagnosis intelligence, ensemble approach, data
mining, emergency department", volume = "6", number = "7", pages = "335-7", }
