Decision support systems in cardiology: A systematic review

Studies in Health Technology and Informatics

Volumn 237, Issue , 2017, Pages 209-214

  Dudchenko, Aleksey ^a   Kopanitsa, Georgy ^a,b  

^a NATIONAL RESEARCH TOMSK POLYTECHNIC UNIVERSITY   (Russian Federation)

^b TOMSK STATE UNIVERSITY OF ARCHITECTURE AND BUILDING   (Russian Federation)

Author keywords

Artificial intelligence; Heart disease; Prediction and diagnosis systems

Indexed keywords

ARTIFICIAL HEART; ARTIFICIAL INTELLIGENCE; CARDIOLOGY; DIAGNOSIS; DISEASES; FUZZY LOGIC; KNOWLEDGE BASED SYSTEMS; NANOTECHNOLOGY; WEARABLE TECHNOLOGY;

CARDIO-VASCULAR DISEASE; DIAGNOSIS SYSTEMS; HEART DISEASE; INTELLIGENT DECISION SUPPORT SYSTEMS; KNOWLEDGE BASE; SYSTEMATIC REVIEW; WEB OF SCIENCE;

DECISION SUPPORT SYSTEMS;

ARTIFICIAL INTELLIGENCE; CARDIOLOGY; CLINICAL DECISION SUPPORT SYSTEM; EXPERT SYSTEM; FUZZY LOGIC; HUMAN; KNOWLEDGE BASE; SOFTWARE;

ARTIFICIAL INTELLIGENCE; CARDIOLOGY; DECISION SUPPORT SYSTEMS, CLINICAL; EXPERT SYSTEMS; FUZZY LOGIC; HUMANS; KNOWLEDGE BASES; SOFTWARE;

EID: 85019433944     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-61499-761-0-209     Document Type: Conference Paper

References (44)

1. Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M and Nichols M. Cardiovascular disease in Europe: Epidemiological update 2016. Eur. Heart J. 2016; 37,42: 3232-3245.
2. Hachamovitch R, et al. Incremental Prognostic Value of Myocardial Perfusion Single Photon Emission Computed Tomography for the Prediction of Cardiac Death. Circulation 1998; 97,6.
3. Johnston ME, Langton KB, Haynes RB and Mathieu A. Effects of Computer-based Clinical Decision Support Systems on Clinician Performance and Patient Outcome: A Critical Appraisal of Research. Ann Intern Med 1994; 120,2: 135.
4. Hunt DL, Haynes RB, Hanna SE and Smith K. Effects of Computer-Based Clinical Decision Support Systems on Physician Performance and Patient Outcomes. JAMA 1998; 280,15: 1339.
5. Bonczek RH, Holsapple CW and Whinston AB. Foundations of decision support systems. Waltham: Academic Press; 1981.
6. Abdel-Motaleb I and Akula R. Artificial intelligence algorithm for heart disease diagnosis using Phonocardiogram signals. In: Proc. 2012 IEEE International Conference on Electro Information Technology, 2012.
7. Ahmad W, Hayee MI, Fitzakerley JL, Burns S and Nordehn G. Heart murmur detection/classification using cochlea-like pre-processing and artificial intelligence. International Journal of Biomedical Engineering and Technology 2011; 7,1: 87-96.
8. Alsalamah M, Amin S and Halloran J. Diagnosis of heart disease by using a radial basis function network classification technique on patients' medical records. RF Wirel. Technol. Biomed. Healthc. Appl. (IMWS-Bio), 2014 IEEE MTT-S Int. Microw. Work. Ser., pp. 1-4.
9. Amin SU, Agarwal K and Beg R. Genetic neural network based data mining in prediction of heart disease using risk factors. In: Proc 2013 IEEE Conference on Information and Communication Technologies, ICT 2013, pp. 1227-1231.
10. Bhuvaneswari Amma NG. An Intelligent Approach Based on Principal Component Analysis and Adaptive Neuro Fuzzy Inference System for Predicting the Risk of Cardiovascular Diseases. In: Proc 2013 Fifth International Conference on Advanced Computing (ICoAC), pp. 241-245.
11. Fathurachman M, Kalsum U, Safitri N and Utomo CP. Heart disease diagnosis using extreme learning based neural networks. In: Proceedings of the 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014, pp. 23-27.
12. Feshki MG and Shijani OS. Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network. In: Proc 2016 Artificial Intelligence and Robotics, IRANOPEN 2016, pp. 48-53.
13. Gjertsson P, Johansson L, Lomsky M, Ohlsson M, Underwood SR and Edenbrandt L. Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy. Clin. Physiol. Funct. Imaging 2011; 31,3: 240-245,.
14. Guidi G, Iadanza E, Pettenati MC, Milli M, Pavone F and Biffi Gentili G. Heart failure artificial intelligence-based computer aided diagnosis telecare system. LNCS 2012; 7251: 278-281.
15. Jian SJ, Chiu RK and Wang S-A. A cloud decision support system for the risk assessment of conorary heart diseases. In: Proc 2012 International Conference on Machine Learning and Cybernetics (ICMLC), 2012, pp. 15-17.
16. Radhimeenakshi S. Classification and prediction of heart disease risk using data mining techniques of Support Vector Machine and Artificial Neural Network. Proc 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 3107-3111.
17. Salari N, Shohaimi S, Najafi F, Nallappan M and Karishnarajah I. A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network. PLoS One 2014; 9,11: E112987.
18. Sonawane JS and Pantil DR. Prediction of Heart Disease Using Multilayer Perceptron Neural Network. Proc Int. Conf. Inf. Commun. Embed. Syst. (ICICES), 2014, pp. 578-582. DOI: 10.1109/ICICES.2014.7033860
19. Tay D, Poh CL and Kitney RI."A novel neural-inspired learning algorithm with application to clinical risk prediction. J Biomed Inform 2015; 54: 305-314.
20. Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput & Applic 2012; 21,7: 1617-1628.
21. Yazdani A and Ramakrishnan K. Performance evaluation of artificial neural network models for the prediction of the risk of heart disease. International Conference for Innovation in Biomedical Engineering and Life Sciences 2016. IFMBE Proceedings 2015; 56: 179-182.
22. Zhang Q, Chen X, Fang Z and Xia S. False arrhythmia alarm reduction in the intensive care unit using data fusion and machine learning. In: Proc 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, pp. 232-235.
23. Critien LV, Khuman AS, Carter J and Ahmadi S. A rule based system for diagnosing and treating chronic heart failure. In: Proc 2014 14th UK Workshop on Computational Intelligence, UKCI 2014.
24. Dai W, Brisimi TS, Adams WG, Mela T, Saligrama V and Paschalidis IC. Prediction of hospitalization due to heart diseases by supervised learning methods. Int. J. Med. Inform 2015; 84,3: 189-197.
25. Fonseca TL, Goliatt L, Campos LCD, Bastos FS, Barra LPS and Dos Santos RW. Machine learning approaches to estimate simulated cardiac ejection fraction from electrical impedance tomography. LNAI 2016; 10022: 235-246.
26. Güraksin GEH. Classification of Heart Sounds Based on the Least Squares Support Vector Machine. Int J Innov Comput Inf Control 2011; 7,12: 7131-7144,.
27. Zheng Y, Guo X, Qin J and Xiao S. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput Methods Programs Biomed 2015; 122,3: 372-383.
28. Acampora G, Lee CS, Vitiello A and Wang M-H. Evaluating cardiac health through semantic soft computing techniques. Soft Comput 2012; 16,7: 1165-1181.
29. Annantha SK, Natarajan SK and Sekar DS. Online based fuzzy analyzer for arrhythmia detection. Communications in Computer and Information Science 2011; 196: 537-544.
30. Bohacik J, Davis DN. Fuzzy rule - based system applied to risk estimation of cardiovascular patients. Journal of multiple-valued logic and soft computing 2013; 20,5: 445-466.
31. Dong W, Huang Z, Ji L and Duan H. A genetic fuzzy system for unstable angina risk assessment. BMC Med Inform Decis Mak 2014; 14: 12.
32. Krajnak M, Xue J, Kaiser W and Balloni W. Combining machine learning and clinical rules to build an algorithm for predicting ICU mortality risk. Computing in Cardiology 2012; 39: 401-404.
33. Lahsasna A, Ainon RN, Zainuddin R and Bulgiba AM. A transparent fuzzy rule-based clinical decision support system for heart disease diagnosis. Communications in Computer and Information Science (CCIS) 2012; 295: 62-71.
34. Paul AK, Shill PC, Rabin MRI and Akhand MAH. Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. In: 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, pp. 145-150.
35. Rahaman S and Hossain MS. A belief rule based clinical decision support system to assess suspicion of heart failure from signs, symptoms and risk factors. In: 2013 International Conference on Informatics, Electronics and Vision, ICIEV 2013. DOI: 10.1109/ICIEV.2013.6572668
36. Yang H and Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease," J Biomed Inform 2015; 58: S171-S182.
37. Fida B, Nazir M, Naveed N and Akram S. Heart disease classification ensemble optimization using Genetic algorithm. In: Proceedings of the 14th IEEE International Multitopic Conference 2011, INMIC 2011, pp. 19-24.
38. Jabbar MA, Deekshatulu BL and Chandra P. An evolutionary algorithm for heart disease prediction. Wireless Networks and Computational Intelligence. Communications in Computer and Information Science 2012; 292: 378-389.
39. Jabbar MA, Deekshatulu BL and Chandra P. Computational intelligence technique for early diagnosis of heart disease. In: Proc 2015 IEEE International Conference on Engineering and Technology (ICETECH), 2015.
40. Salah RB, Hadidi T and Chabchoub S. Intelligent diagnosis method of cardiovascular anomalies using medical signal processing. In: 2015 World Congress on Information Technology and Computer Applications, WCITCA 2015.
41. Arsanjani R, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. Journal of Nuclear Cardiology 2013; 20,4: 553-562.
42. Bashir S, Qamar U and Younus Javed M. An ensemble based decision support framework for intelligent heart disease diagnosis. In: Proc Int. Conf. Inf. Soc. (i-Society 2014), pp. 259-264.
43. Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic 2016; 1-9.
44. Boursalie O, Samavi R and Doyle TE. M4CVD: Mobile machine learning model for monitoring cardiovascular disease. Procedia Computer Science 2015; 63: 384-391.