Abstract
In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.
Rule | Orthop | HR | Clidys | Wheeze | PND | Hepa | Diagnosis |
---|---|---|---|---|---|---|---|
1 | Mild | Mild | Mild | Mild | Mild | Mild | Very mild |
2 | Mild | Mild | Mild | Mild | Mild | Moderate | Very mild |
3 | Mild | Mild | Mild | Mild | Moderate | Moderate | Mild |
4 | Mild | Mild | Mild | Moderate | Moderate | Moderate | Mild |
5 | Mild | Mild | Moderate | Moderate | Moderate | Moderate | Mild |
6 | Mild | Moderate | Moderate | Moderate | Moderate | Moderate | Moderate |
7 | Moderate | Moderate | Moderate | Moderate | Moderate | Moderate | Moderate |
8 | Moderate | Moderate | Moderate | Moderate | Moderate | Mild | Moderate |
9 | Moderate | Moderate | Moderate | Moderate | Mild | Mild | Moderate |
10 | Moderate | Moderate | Moderate | Mild | Mild | Mild | Moderate |
11 | Moderate | Moderate | Mild | Mild | Mild | Mild | Moderate |
12 | Moderate | Mild | Mild | Mild | Mild | Mild | Mild |
13 | Moderate | Mild | Mild | Mild | Mild | Severe | Moderate |
14 | Moderate | Mild | Mild | Mild | Severe | Severe | Moderate |
15 | Mild | Mild | Mild | Mild | Severe | Severe | Moderate |
16 | Mild | Mild | Mild | Severe | Severe | Severe | Moderate |
17 | Mild | Mild | Severe | Severe | Severe | Severe | Severe |
18 | Mild | Severe | Severe | Severe | Severe | Severe | Very severe |
19 | Severe | Severe | Severe | Severe | Severe | Severe | Very severe |
20 | Moderate | Moderate | Moderate | Severe | Severe | Severe | Severe |
21 | Moderate | Moderate | Severe | Severe | Severe | Severe | Severe |
22 | Moderate | Severe | Severe | Severe | Severe | Severe | Very severe |
23 | Mild | Severe | Mild | Severe | Mild | Severe | Moderate |
24 | Mild | Moderate | Severe | Mild | Severe | Moderate | Moderate |
25 | Moderate | Mild | Severe | Severe | Moderate | Mild | Moderate |
26 | Severe | Moderate | Mild | Severe | Mild | Moderate | Moderate |
27 | Moderate | Mild | Moderate | Mild | Severe | Severe | Moderate |
28 | Mild | Mild | Moderate | Moderate | Severe | Severe | Moderate |
29 | Moderate | Moderate | Mild | Mild | Severe | Severe | Severe |
30 | Severe | Severe | Moderate | Moderate | Mild | Mild | Moderate |
31 | Severe | Mild | Mild | Severe | Moderate | Moderate | Moderate |
32 | Moderate | Mild | Mild | Moderate | Severe | Severe | Severe |
33 | Mild | Moderate | Severe | Severe | Mild | Moderate | Moderate |
34 | Severe | Mild | Mild | Mild | Mild | Mild | Mild |
35 | Severe | Severe | Severe | Severe | Severe | Mild | Very severe |
36 | Severe | Severe | Severe | Severe | Mild | Mild | Severe |
37 | Severe | Severe | Severe | Mild | Mild | Mild | Severe |
38 | Severe | Severe | Mild | Mild | Mild | Mild | Moderate |
39 | Severe | Mild | Mild | Mild | Mild | Mild | Moderate |
40 | Severe | Moderate | Moderate | Moderate | Moderate | Moderate | Moderate |
41 | Severe | Severe | Moderate | Moderate | Moderate | Moderate | Moderate |
42 | Severe | Severe | Severe | Moderate | Moderate | Moderate | Severe |
43 | Severe | Severe | Severe | Severe | Moderate | Moderate | Severe |
44 | Severe | Severe | Severe | Severe | Severe | Moderate | Very severe |
45 | Severe | Severe | Severe | Mild | Moderate | Moderate | Severe |
46 | Severe | Severe | Severe | Mild | Mild | Moderate | Severe |
47 | Severe | Moderate | Moderate | Mild | Severe | Mild | Moderate |
Clidys, dyspnea while climbing; Hepa, hepatomegaly; HR, heart rate; Orthop, orthopnea; PND, paroxysmal nocturnal dyspnea; Wheez, wheezing.
S/N | Enalapril | Linsonopril | Captropril | Bisoprolol | Hydralaxine | Metroprolol | Others | Therapy |
---|---|---|---|---|---|---|---|---|
1 | Mil | Mild | 0 | 0 | Mild | Mild | Moderate | Mild |
2 | Mild | Moderate | Moderate | Moderate | 0 | 0 | Moderate | Moderate |
3 | Moderate | Moderate | 0 | Moderate | 0 | 0 | Moderate | Moderate |
4 | 0 | Moderate | Moderate | Moderate | Severe | Severe | Moderate | Severe |
5 | Moderate | Moderate | Severe | Moderate | 0 | 0 | Severe | Moderate |
6 | Severe | Severe | 0 | Severe | Severe | 0 | Moderate | Moderate |
7 | 0 | Moderate | 0 | Severe | 0 | Moderate | Moderate | Moderate |
8 | 0 | Mild | Mild | Mild | Mild | 0 | Mild | Mild |
9 | Moderate | Moderate | Moderate | Moderate | 0 | 0 | Moderate | Moderate |
10 | Moderate | 0 | 0 | Moderate | 0 | Mild | Mild | Moderate |
11 | Mild | Moderate | Mild | Moderate | 0 | 0 | Mild | Mild |
12 | Moderate | Severe | Severe | Moderate | 0 | Mild | Mild | Moderate |
13 | Severe | Moderate | Mild | Mild | Moderate | Severe | 0 | Moderate |
14 | Severe | Mild | Mild | Moderate | 0 | 0 | Moderate | Moderate |
15 | Moderate | Moderate | Severe | Severe | Severe | 0 | 0 | Severe |
16 | Mild | Moderate | Severe | Severe | Moderate | Moderate | Moderate | Severe |
17 | 0 | 0 | 0 | Severe | Severe | Severe | 0 | Severe |
18 | Severe | Severe | Moderate | 0 | 0 | 0 | Severe | Severe |
19 | Mild | Severe | Mild | Mild | Mild | 0 | 0 | Moderate |
20 | Mild | 0 | 0 | 0 | Severe | Mild | Mild | Moderate |
21 | 0 | 0 | Severe | Severe | 0 | 0 | Mild | Severe |
22 | Severe | Moderate | Moderate | Mild | 0 | 0 | 0 | Moderate |
23 | Mild | Moderate | Severe | 0 | Mild | Mild | 0 | Moderate |
24 | 0 | 0 | Mild | Mild | 0 | 0 | Mild | Mild |
25 | Mild | Mild | Moderate | Moderate | Severe | Severe | 0 | Severe |
26 | 0 | Mild | Severe | Moderate | Moderate | Severe | 0 | Moderate |
S/N, Serial number.
References
1. Akinyokun OC, Shogbon JA. A framework of neuro-fuzzy expert system for capital investment appraisal. J ICAN 2006;556–65.Search in Google Scholar
2. Obot OU, Uzoka FM. A framework for application of neuro-case-rule base hybridization in medical diagnosis. Appl Soft Comput 2009;9:245–53.10.1016/j.asoc.2008.01.010Search in Google Scholar
3. Szolovits P. Uncertainty and decisions in medical informatics. Methods Inf Med 1995;34:111–21.10.1055/s-0038-1634594Search in Google Scholar
4. Song Q, Kasabov N. A novel generic higher-order TSK fuzzy model for prediction and applications for medical decision support. In: Proc. 8th Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), 10–12. December, 2003, Sydney, NSW, Australia, 2003:241–5.Search in Google Scholar
5. Pople HE. Heuristic methods for imposing structure on ill-structured problems: the structuring of medical diagnostics. In: Szolovits P, editor. Artificial intelligence in medicine. Boulder, CO: Westview Press, 1982:chap 5.Search in Google Scholar
6. Miller RA, Pople HE, Myers JD. INTERSIT-1: an experimental computer based diagnostic consultant for general internal medicine. N Engl J Med 1982;316:250–8.Search in Google Scholar
7. Podgorelec V, Kokol P. Towards more optimal medical diagnosing with evolutionary algorithms. J Med Syst 2001;25:195–219.10.1023/A:1010733016906Search in Google Scholar
8. Ochi-Okorie AS. Disease diagnosis validation in TROPIX using CBR. Artif Intell Med 1998;12:43–60.10.1016/S0933-3657(97)00039-0Search in Google Scholar
9. Timpka T, Padgham L, Hedblom P, Wallin S, Tibblin G. A hypertext knowledge base for primary care. ACM 1989;23: 221–228.Search in Google Scholar
10. Uzoka F-ME, Famuyiwa FO. A framework for the application of knowledge technology to the management of diseases. Int J Health Care Qual Assur 2004;17:194–204.10.1108/09526860410541513Search in Google Scholar
11. Akinyokun OC, Adeniyi OA. Experimental study of intelligent computer aided medical diagnostics and therapy. AMSE J Model Simul Control 1991;27:1–20.Search in Google Scholar
12. Bonnisone PP, Geobel K, Khedkar PS. Hybrid soft computing systems: industrial and commercial applications. Proc IEEE 1999;87:1641–67.10.1109/5.784245Search in Google Scholar
13. Lefebvre C, Principe J. Object-oriented artificial neural network implementations. World Congr Neural Networks 1993;4:436–9.Search in Google Scholar
14. Lefebvre C, Principe J. NeuroSolution 6.0. Gainesville, FL: NeuroDimension, 2007.Search in Google Scholar
15. Economou GP, Hallas JA, Mariatos EP, Goutis CE. Artificial networks in medical decision making systems: an application to pulmonary diseases’ diagnosis through VHDL synthesis. Proc Eur Design Test Conf 1995:590.Search in Google Scholar
16. Gomez-Ruiz JA, Jerez-Aragones JM, Munoz-Perez J. A neural network based model for prognosis of early breast cancer. Appl Intell 2004;20:231–8.10.1023/B:APIN.0000021415.88365.c4Search in Google Scholar
17. Daniels JE, Cayton RM. Cadosa: a fuzzy expert system for differential diagnosis of obstructive sleep apnoea and related conditions. Expert Syst Appl 1997;12:163–77.10.1016/S0957-4174(96)00091-7Search in Google Scholar
18. Wainer J, Sandri S. Fuzzy temporal/categorical information in diagnosis. J Intell Inf Syst 1999;13:9–29.10.1023/A:1008702804774Search in Google Scholar
19. Obot OU, Uzoka FM. Fuzzy rule-based framework for the management of tropical diseases Int J Med Eng Inf 2008;1:7–17.10.1504/IJMEI.2008.019466Search in Google Scholar
20. Marantz PR, Tobin JN, Watsertheil-Samaler S, Steingart RM, Wexler JP, Budner N. The relationship between left ventricular systolic function and congestive heart failure diagnosed by clinical criteria. Circulation 1988;77:607–12.10.1161/01.CIR.77.3.607Search in Google Scholar
21. Obot OU, Akinyokun OC, Udoh SS. Application of soft computing methodologies to the management of hypertension. J Comput Appl 2008;15:131–47.Search in Google Scholar
22. Anderson GR. Your guide to health. Pune, India: Oriental Watchman Publishing House, 1976.Search in Google Scholar
23. Sutton GE, Fox KM. Heart diseases (pathological, clinical and investigatory features). Pune, India: Lippincot Company, 1990:43–47.Search in Google Scholar
24. Braunwald E, Grossman W. Clinical aspects in heart failure. In: Braunwald E, editor. Heart disease: a textbook in cardiovascular medicine, 5th ed. 1997:445–70.Search in Google Scholar
25. Davie AP, Francis CM, Caruana L, Sutherland GR, McMurray JJ. Assessing diagnosis in heart failure: which features are any use? QJM 1997;90:335–9.10.1093/qjmed/90.5.335Search in Google Scholar
26. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult. Circulation 2005;112:1825–52.Search in Google Scholar
27. Hobbs R, Boyle A. Disease Management Project ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary: a report of the American College of Cardiology/American Heart Association, 2005. Available at: http://circ.ahajournals.org/content/112/12/1825.full.pdf+html?sid=d3454c8f-0fe5-4873-b4d7-459164dc8be1. Accessed on December 12, 2010.Search in Google Scholar
28. Abraham A, Nath B. Evolutionary design of fuzzy control systems – a hybrid approach. In: 6th International Conference on Control, Automation, Robotics and Vision (ICARCV, 2000). 2000. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.1853&rep=rep1&type=pdf. Accessed on April 15, 2007.Search in Google Scholar
29. Takagi T, Sugeno M. Derivation of fuzzy control rules from human operators control actions. In: Proceedings of the IAFC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 1993:55–60.10.1016/S1474-6670(17)62005-6Search in Google Scholar
30. Shogbon JA. Neuro-fuzzy expert system for appraisal of capital investment, PhD thesis dissertation. Akure: Federal University of Technology, 2003.Search in Google Scholar
31. Luneski A, Konstantinidis E, Bamidis PD. Affective medicine: a review of affective computing efforts in medical informatics. MIM 2010;49:207–18. Available at: http://www.schattauer.de/en/magazine/subject-areas/journals-a-z/methods/issue/special/manuscript/12987/show.html. Accessed on September 5, 2012.Search in Google Scholar
32. Uzoka FM, Osuji J, Obot O. Clinical decision support system (DSS) in the diagnosis of malaria: a case comparison of two soft computing methodologies. Expert Syst Appl J 2011;38:1537–53.10.1016/j.eswa.2010.07.068Search in Google Scholar
©2013 by Walter de Gruyter Berlin Boston