Skip to main content

Advertisement

Springer Nature Link
Log in

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., Advances in knowledge discovery and data mining. AAAI Press/MIT Press, Boston, 1996.

    Google Scholar 

  2. Cios, K.J., Teresinska, A., Konieczna, S., Potocka, J., and Sharma, S., Diagnosing myocardial perfusion SPECT bull’s-eye maps-a knowledge discovery approach. IEEE Eng Med Biol. 19(4):17–25, 2000.

    Article  CAS  Google Scholar 

  3. Cios K.J., Moore G.W., Medical data mining and knowledge discovery: an overview. Heidelberg: Springer, pp 1-16, 2000.

  4. Cios, K.J., and Kurgan, L.A., Trends in data mining and knowledge discovery. Springer, 2002.

  5. Cios, K.J., Pedrycz, W., and Swiniarski, R., Mining methods for knowledge discovery. Kluwer Academic Publishers, 1998.

  6. Fernandez, A., Jesus, M.J., and Herrera, F., On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets. Expert Syst. Appl. 36:9805–9812, 2009.

    Article  Google Scholar 

  7. Chang, P.C., and Liao, T.W., Combing SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl. Soft Comput. 6(2):198–206, 2006.

    Article  Google Scholar 

  8. Pendharkar, P.C., Rodger, J.A., Yaverbaum, G.J., Herman, N., and Benner, M., Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Syst. Appl. 17:223–232, 1999.

    Article  Google Scholar 

  9. Lai, R.K., Fan, C.Y., Huang, W.H., and Chang, P.C., Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Syst. Appl. 3:3761–3773, 2009.

    Article  Google Scholar 

  10. Giri, D., Acharya, U.R., Martis, R.J., Sree, S.V., Lim, T.C., Ahamed, T., and Suri, J., Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl.-Based Syst. 37:274–282, 2013.

    Article  Google Scholar 

  11. Babaoglu, İ., Findik, O., and Ülker, E., A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst. Appl. 37(4):3177–3183, 2010.

    Article  Google Scholar 

  12. Patil, B.M., Joshi, R.C., and Toshniwal, D., Hybrid prediction model for type-2 diabetic patients. Expert Syst. Appl. 37(12):8102–8108, 2010.

    Article  Google Scholar 

  13. Çalişir, D., and Dogantekin, E., A new intelligent hepatitis diagnosis system: PCA–LSSVM. Expert Syst. Appl. 38(8):10705–10708, 2011.

    Article  Google Scholar 

  14. Sweilam, N.H., Tharwat, A.A., and Moniem, N.A., Support vector machine for diagnosis cancer disease: a comparative study. Egypt. Inform. J. 11(2):81–92, 2010.

    Article  Google Scholar 

  15. Abdi, J.M., and Giveki, D., Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules. Eng. Appl. Artif. Intell. 26(p):603–608, 2013.

    Article  Google Scholar 

  16. Bhardwaj, A., and Tiwari, A., Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Appl.:1–15, 2015.

  17. Fei, S.W., Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine. Expert Syst. Appl. 37(10):6748–6752, 2010.

    Article  Google Scholar 

  18. Chen, H.L., Liu, D.Y., Yang, B., Liu, J., and Wang, G., A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Syst. Appl. 38(9):11796–11803, 2011.

    Article  Google Scholar 

  19. Alkım, E., Gürbüz, E., and Kılıç, E., A fast and adaptive automated disease diagnosis method with an innovative neural network model. Neural Netw. 33:88–96, 2012.

    Article  PubMed  Google Scholar 

  20. López, M., Ramírez, J., Górriz, J.M., Álvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R., Padilla, P., Gómez-Río, M., and Initiative, A.’s.D.N., Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease. Neurocomputing. 74(8):1260–1271, 2011.

    Article  Google Scholar 

  21. Park, Y.J., Chun, S.H., and Kim, B.C., Cost-sensitive case-based reasoning using a genetic algorithm: application to medical diagnosis. Artif. Intell. Med. 51(2):133–145, 2011.

    Article  PubMed  Google Scholar 

  22. Latifoğlu, F., Polat, K., Kara, S., and Güneş, S., Medical diagnosis of atherosclerosis from carotid artery Doppler signals using principal component analysis (PCA), k-NN based weighting pre-processing and artificial immune recognition system (AIRS). J. Biomed. Inform. 41(1):15–23, 2008.

    Article  PubMed  Google Scholar 

  23. Zheng, B., Yoon, S.W., and Lam, S.S., Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41(4):1476–1482, 2014.

    Article  Google Scholar 

  24. Yeh, D.Y., Cheng, C.H., and Chen, Y.W., A predictive model for cerebrovascular disease using data mining. Expert Syst. Appl. 38(7):8970–8977, 2011.

    Article  Google Scholar 

  25. Eom, J.H., Kim, S.C., and Zhang, B.T., AptaCDSS-E: a classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert Syst. Appl. 34(4):2465–2479, 2008.

    Article  Google Scholar 

  26. Übeyli, E.D., and Doğdu, E., Automatic detection of erythemato-squamous diseases using k-means clustering. J. Med. Syst. 34(2):179–184, 2010.

    Article  PubMed  Google Scholar 

  27. Ozcift, A., and Gulten, A., Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput. Methods Prog. Biomed. 104(3):443–451, 2011.

    Article  Google Scholar 

  28. Jerez, J.M., Molina, I., García-Laencina, P.J., Alba, E., Ribelles, N., Martín, M., and Franco, L., Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif. Intell. Med. 50(2):105–115, 2010.

    Article  PubMed  Google Scholar 

  29. Das, R., and Sengur, A., Evaluation of ensemble methods for diagnosing of valvular heart disease. Expert Syst. Appl. 37(7):5110–5115, 2010.

    Article  Google Scholar 

  30. Sartakhti, J.S., Zangooei, M.H., and Mozafari, K., Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Comput. Methods Prog. Biomed. 108(2):570–579, 2012.

    Article  Google Scholar 

  31. Nahar, J., Imam, T., Tickle, K.S., and Chen, Y.P.P., Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst. Appl. 40(1):96–104, 2013.

    Article  Google Scholar 

  32. Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W. and Thompson, P.M., Comparison of AdaBoost and support vector machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Trans. Med. Imaging 29(1), p. 30, 2010

  33. Er, O., Yumusak, N., and Temurtas, F., Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl. 37(12):7648–7655, 2010.

    Article  Google Scholar 

  34. Er, O., Temurtas, F., and Tanrıkulu, A.Ç., Tuberculosis disease diagnosis using artificial neural networks. J. Med. Syst. 34(3):299–302, 2010.

    Article  PubMed  Google Scholar 

  35. Kampouraki, A., Manis, G., and Nikou, C., Heartbeat time series classification with support vector machines. IEEE Trans. Inf. Technol. Biomed. 13(4):512–518, 2009.

    Article  PubMed  Google Scholar 

  36. Elveren, E., and Yumuşak, N., Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J. Med. Syst. 35(3):329–332, 2011.

    Article  PubMed  Google Scholar 

  37. Cho, B.H., Yu, H., Lee, J., Chee, Y.J., Kim, I.Y., and Kim, S.I., Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels. IEEE Trans. Inf. Technol. Biomed. 12(2):247–256, 2008.

    Article  PubMed  Google Scholar 

  38. Lee, C.S., and Wang, M.H., A fuzzy expert system for diabetes decision support application. IEEE Trans. Syst. Man Cybernatics. 41(1):139–153, 2011.

    Article  Google Scholar 

  39. Sengur, A., An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases. Comput. Biol. Med. 38:329–338, 2008.

    Article  PubMed  Google Scholar 

  40. Sengur, A., An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Syst. Appl. 35:214–222, 2008.

    Article  Google Scholar 

  41. Arsene, O., Dumitrache, I., and Mihu, I., Expert system for medicine diagnosis using software agents. Expert Syst. Appl. 42:1825–1834, 2015.

    Article  Google Scholar 

  42. Pal, D., Mandana, K.M., Pal, S., Sarkar, D., and Chakraborty, C., Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl.-Based Syst. 36:162–174, 2012.

    Article  Google Scholar 

  43. Hariharana, M., Polat, K., and Sindhu, R., A new hybrid intelligent system for accuratedetection of Parkinson’s disease. Comput. Methods Med. 113:904–913, 2014.

    Google Scholar 

  44. Keles, A., ESTDD: expert system for thyroid diseases diagnosis. Expert Syst. Appl. 34:242–246, 2008.

    Article  Google Scholar 

  45. Chen, H.L., Huang, C.C., Yu, X.G., Xu, X., Sun, X., Wang, G., and Wang, S.J., An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst. Appl. 40:263–271, 2013.

    Article  Google Scholar 

  46. Ucar, T., Karahoca, A., and Karahoca, D., Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Comput. & Applic. 23:471–483, 2013.

    Article  Google Scholar 

  47. Uguz, H., Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput. & Applic. 21:1617–1628, 2012.

    Article  Google Scholar 

  48. Muthukaruppan, S., and Er, M.J., A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Syst. Appl. 39:11657–11665, 2012.

    Article  Google Scholar 

  49. Seera, M., and Lim, C.P., Hybrid intelligent system for medical data classification. Expert Syst. Appl. 41:2239–2249, 2014.

    Article  Google Scholar 

  50. Ubeyli, E.D., Adaptive neuro-fuzzy inference Systems for Automatic Detection of breast cancer. J. Med. Syst. 33:353–358, 2009.

    Article  PubMed  Google Scholar 

  51. Kar, S., Das, S., and Ghosh, P.K., Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15:243–259, 2014.

    Article  Google Scholar 

  52. Papageorgiou, E.I., A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 11:500–513, 2011.

    Article  Google Scholar 

  53. Sanz, J.A., Galar, M., Jurio, A., Brugos, A., Pagola, M., and Bustince, H., Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system. Appl. Soft Comput. 20:103–111, 2014.

    Article  Google Scholar 

  54. Pal, D., Mandana, K.M., Pal, S., Sarkar, D., and Chakraborty, C., Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl.-Based Syst. 36:162–174, 2012.

    Article  Google Scholar 

  55. Kannathal N., Lim, C.M., Acharya, U.R., Sadasivan, P.K., Cardiac state diagnosis using adaptive neuro-fuzzy technique. Med. Eng. Phys. 28, pp 809–815.

  56. Khatibi, V., and Montazer, G.A., A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Expert Syst. Appl. 37(12):8536–8542, 2010.

    Article  Google Scholar 

  57. Uzoka, F.M.E., Osuji, J., and Obot, O., Clinical decision support system (DSS) in the diagnosis of malaria: a case comparison of two soft computing methodologies. Expert Syst. Appl. 38(3):1537–1553, 2011.

    Article  Google Scholar 

  58. Ciabattoni, A., Picado, M.D., Vetterlein, T., and El-Zekey, M., Formal approaches to rule-based systems in medicine: the case of CADIAG-2. Int. J. Approx. Reason. 54:132–148, 2013.

    Article  Google Scholar 

  59. Ciabattoni, A., and Vetterlein, T., On the (fuzzy) logical content of CADIAG-2. Fuzzy Sets Syst. 161:1941–1958, 2010.

    Article  Google Scholar 

  60. Muino, D. P., A probabilistic interpretation of the medical expert system CADIAG-2. Soft. Comput. 15:2013–2020, 2011.

  61. Pal, D., Mandana, K. M., Pal, S., Sarkar, D., and Chakraborty, C., Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl.-Based Syst. 36:162–174, 2012.

  62. Chaves, R., Ramírez, J., Górriz, J.M., Puntonet, C.G., and Alzheimer’s Disease Neuroimaging Initiative, Association rule-based feature selection method for Alzheimer’s disease diagnosis. Expert Syst. Appl. 39(14):11766–11774, 2012.

    Article  Google Scholar 

  63. Ell, S.W., Weinstein, A., and Ivry, R.B., Rule-based categorization deficits in focal basal ganglia lesion and Parkinson’s disease patients. Neuropsychologia. 48:2974–2986, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Astrom, F., and Koker, R., A parallel neural network approach to prediction of Parkinson’s disease. Expert Syst. Appl. 38:12470–12474, 2011.

    Article  Google Scholar 

  65. Kong, G., Xu, D.-U., Body, R., Yang, J.-B., Jones, K.M., and Carley, S., A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. Eur. J. Oper. Res. 219:564–573, 2012.

    Article  Google Scholar 

  66. Kumar, A.K., Singh, Y., and Sanyal, S., Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU. Expert Syst. Appl. 36:65–71, 2009.

    Article  Google Scholar 

  67. Lisboa, P.J., Etchells, T.A., Jarman, I.H., Hane Aung, M.S., Chabaud, S., Bachelot, T., Perol, D., Gargi, T., Bourdes, V., Bonnevay, S., and Negrier, S., Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer. Neural Netw. 21:414–426, 2008.

    Article  PubMed  Google Scholar 

  68. Mykowiecka, A., Marciniak, M., and Kupsc, A., Rule-based information extraction from patients’ clinical data. J. Biomed. Inform. 42:923–936, 2009.

    Article  PubMed  Google Scholar 

  69. Price, A., Filoteo, J.V., and Todd, M.W., Rule-based category learning in patients with Parkinson’s disease. Neuropsychologia. 47:1213–1226, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Seto, E., Leonard, K.J., Cafazzo, J.A., Barnsley, J., Masino, C., and Ross, H.J., Developing healthcare rule-based expert systems: case study of a heart failure telemonitoring system. Int. J. Med. Inform. 81:556–565, 2012.

    Article  PubMed  Google Scholar 

  71. Wei, M.H., Cheng, C.H., and Li, J.Y., Discovering medical resource utilization in total knee arthroplasty (TKA) using rule-based method. Arch. Gerontol. Geriatr. 55:157–164, 2012.

    Article  PubMed  Google Scholar 

  72. Sevastianov, P., Dymova, L., and Bartosiewicz, P., A framework for rule-base evidential reasoning in the interval setting applied to diagnosing type 2 diabetes. Expert Syst. Appl. 39(4):4190–4200, 2012.

    Article  Google Scholar 

  73. Nahar, J., Imam, T., Tickle, K.S., and Chen, Y.P.P., Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst. Appl. 40(4):1086–1093, 2013.

    Article  Google Scholar 

  74. Jung, H., Yang, J., Woo, J.I., Lee, B.M., Ouyang, J., Chung, K., and Lee, Y., Evolutionary rule decision using similarity based associative chronic disease patients. Clust. Comput. 18(1):279–291, 2015.

    Article  Google Scholar 

  75. Toro, C., Sanchez, E., Carrasco, E., Mancilla-Amaya, L., Sanín, C., Szczerbicki, E., Graña, M., Bonachela, P., Parra, C., Bueno, G., and Guijarro, F., Using set of experience knowledge structure to extend a rule set of clinical decision support system for alzheimer’s disease diagnosis. Cybern. Syst. 43(2):81–95, 2012.

    Article  Google Scholar 

  76. Chen, R.C., Huang, Y.H., Bau, C.T., and Chen, S.M., A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst. Appl. 39(4):3995–4006, 2012.

    Article  Google Scholar 

  77. Stoean, R., and Stoean, C., Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Syst. Appl. 40(7):2677–2686, 2013.

    Article  Google Scholar 

  78. Sarkar, B.K., Sana, S.S., and Chaudhuri, K., A genetic algorithm-based rule extraction system. Appl. Soft Comput. 12(1):238–254, 2012.

    Article  Google Scholar 

  79. Ang, J.H., Tan, K.C., and Mamun, A.A., An evolutionary memetic algorithm for rule extraction. Expert Syst. Appl. 37(2):1302–1315, 2010.

    Article  Google Scholar 

  80. Mohamed, M.H., Rules extraction from constructively trained neural networks based on genetic algorithms. Neurocomputing. 74(17):3180–3192, 2011.

    Article  Google Scholar 

  81. Kumar, Y., and Sahoo, G., Prediction of different types of liver diseases using rule based classification model. Technol. Health Care. 21(5):417–432, 2013.

    PubMed  Google Scholar 

  82. Sharaf-El-Deen D.A., Moawad I.F., Khalifa M.E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38:9, Springer, 2014.

  83. Ocampo, E., Maceiras, M., Herrera, S., Maurente, C., Rodríguez, D., and Sicilia, M.A., Comparing Bayesian inference and case-based reasoning as support techniques in the diagnosis of acute bacterial meningitis. Expert Syst. Appl. 38(8):10343–10354, 2011.

    Article  Google Scholar 

  84. Begum, S., Ahmed, M.U., Funk, P., Xiong, N., and Folke, M., Case-based reasoning systems in the health sciences: a survey of recent trends and developments. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(4):421–434, 2011.

    Article  Google Scholar 

  85. Huang, M.-L., Hung, Y.-H., Lee, W.-M., Li, R.K., and Wang, T.-H., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J. Med. Syst. 36(2):407–414, 2012.

    Article  PubMed  Google Scholar 

  86. Teodorović, D., Šelmić, M., and Mijatović-Teodorović, L., Combining case-based reasoning with bee Colony ptimization for dose planning in well differentiated thyroid cancer treatment. Expert Syst. Appl. 40(6):2147–2155, 2013.

    Article  Google Scholar 

  87. Depeursinge, A., Vargas, A., Gaillard, F., Platon, A., Geissbuhler, A., Poletti, P.-A., and Müller, H., Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows. Int. J. Comput. Assist. Radiol. Surg. 7(1):97–110, 2012.

    Article  PubMed  Google Scholar 

  88. Bichindaritz, I., and Montani, S., Advances in case-based reasoning in the health sciences. Artif. Intell. Med. 51:75–79, 2011.

    Article  PubMed  Google Scholar 

  89. Chattopadhyay, S., Banerjee, S., Rabhi, F.A., and Acharya, U.R., A Case-Based Reasoning system for complex medical diagnosis. Expert. Syst. 30(1):12–20, 2013.

    Article  Google Scholar 

  90. Fana, C.Y., Chang, P.C., Lin, J.J., and Hsieh, J.C., A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11:632–644, 2011.

    Article  Google Scholar 

  91. Guessoum, S., Tayeb, L.M., and Lieber, J., RESPIDIAG: a case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease. Expert Syst. Appl. 41:267–273, 2014.

    Article  Google Scholar 

  92. Marling, C., Montani, S., Bichindaritz, I., and Funk, P., Synergistic case-based reasoning in medical domains. Expert Syst. Appl. 41:249–259, 2014.

    Article  Google Scholar 

  93. Huang, M.L., Hung, Y.H., Lee, W.M., Li, R.K., and Wang, T.H., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J. Med. Syst. 36(2):407–414, 2012.

    Article  PubMed  Google Scholar 

  94. Ping, X.-O., Tseng, Y.-J., Lin, Y.-P., Chiu, H.-J., Lai, F., Liang, J.-D., Huang, G.-T., and Yang, P.-M., A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients. Comput. Ind. 69:12–21, 2015.

    Article  Google Scholar 

  95. Lin, R.H., and Chuang, C.L., A hybrid diagnosis model for determining the types of the liver disease. Comput. Biol. Med. 40(7):665–670, 2010.

    Article  PubMed  Google Scholar 

  96. McSherry, D., Conversational case-based reasoning in medical decision making. Artif. Intell. Med. 52(2):59–66, 2011.

    Article  PubMed  Google Scholar 

  97. Chuang, C.L., Case-based reasoning support for liver disease diagnosis. Artif. Intell. Med. 53(1):15–23, 2011.

    Article  PubMed  Google Scholar 

  98. Hsu, K.H., Chiu, C., Chiu, N.H., Lee, P.C., Chiu, W.K., Liu, T.H., and Hwang, C.J., A case-based classifier for hypertension detection. Knowl.-Based Syst. 24(1):33–39, 2011.

    Article  Google Scholar 

  99. Sharaf-El-Deen, D.A., Moawad, I.F., and Khalifa, M.E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38(2):1–11, 2014.

    Article  Google Scholar 

  100. Long, N.C., Meesad, P., and Unger, H., A highly accurate firefly based algorithm for heart disease prediction. Expert Syst. Appl. 42(21):8221–8231, 2015.

    Article  Google Scholar 

  101. Kumar, S.U., and Inbarani, H.H., Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft. Comput.:1–13, 2016.

  102. Chen, L., Li, X., Yang, Y., Kurniawati, H., Sheng, Q.Z., Hu, H.Y., and Huang, N., Personal health indexing based on medical examinations: a data mining approach. Decis. Support. Syst. 81:54–65, 2016.

    Article  Google Scholar 

  103. Prasad, V., Rao, T.S., and Babu, M.S.P., Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms. Soft. Comput. 20(3):1179–1189, 2016.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM University, Delhi NCR, Sonipat, Haryana, India

    Shalini Gambhir & Sanjay Kumar Malik

  2. Department of Information Technology, KIET Group of Institution, Ghaziabad, India

    Yugal Kumar

Authors
  1. Shalini Gambhir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Kumar Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yugal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yugal Kumar.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gambhir, S., Malik, S.K. & Kumar, Y. Role of Soft Computing Approaches in HealthCare Domain: A Mini Review. J Med Syst 40, 287 (2016). https://doi.org/10.1007/s10916-016-0651-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0651-x

Keywords

Search

Navigation