ACO Inspired Computer-aided Detection/Diagnosis (CADe/CADx) Model for Medical Data Classification

Author(s): Anuradha Dhull*, Kavita Khanna, Akansha Singh, Gaurav Gupta.

Journal Name: Recent Patents on Computer Science

Volume 12 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system.

Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature that keeping a balance between these performance parameters is a real challenge.

Methods: In order to achieve the desired objective, the following two contributions have been proposed to improve diagnosis accuracy: 1) an unsupervised feature selection approach based on ACO Meta-heuristic is used to design the CADe system, and 2) an ACO assisted decision tree classifier technique is employed to make CADx system.

Results: Three popular UCI (Wisconsin Breast Cancer, Pima Indian Diabetes and Liver Disorder) medical domain datasets have been used to evaluate the performance of the proposed model. The exploratory result analysis shows the efficiency of the proposed work as compared to existing work.

Keywords: Feature selection and ranking, fuzzy decision tree classifier, Distinct Class Split Measure (DCSM), Ant Colony Optimization (ACO), Computer Aided Detection (CADe), Computer Aided Diagnosis (CADx).

[1]
X. Song, B. Hwong, G. Matos, A. Rudorfer, C. Nelson, M. Han, and A. Girenkov, "Understanding requirements for computer-aided healthcare workflows: experiences and challenges", In: 28th International Conference on Software Engineering (ICSE 2006), Shanghai, China, pp. 930-934. 2006
[http://dx.doi.org/10.1145/1134285.1134455]
[2]
B. Chaudhry, J. Wang, S. Wu, M. Maglione, W. Mojica, E. Roth, S.C. Morton, and P.G. Shekelle, "Systematic review: impact of health information technology on quality, efficiency, and costs of medical care", Ann. Intern. Med., vol. 144, no. 10, pp. 742-752, 2006. [http://dx.doi.org/10.7326/0003-4819-144-10-200605160-00125]. [PMID: 16702590].
[3]
F. Chabat, D.M. Hansell, and G-Z. Yang, "Computerized decision support in medical imaging", IEEE Eng. Med. Biol. Mag., vol. 19, no. 5, pp. 89-96, 2000. [http://dx.doi.org/10.1109/51.870235]. [PMID: 11016034].
[4]
J. Bernatavičienė, G. Dzemyda, O. Kurasova, V. Marcinkevičius, and V. Medvedev, The problem of visual analysis of multidimensional medical data, in models and algorithms for global optimization.Models and Algorithms for Global Optimization.Springer: Boston, MA, USA Vol. 4 2007, . [https://doi.org/10.1007/978-0-387-36721-7_17]
[5]
P. Meesad, and G.G. Yen, "Combined numerical and linguistic knowledge representation and its application to medical diagnosis", IEEE Trans. Syst. Man Cybern. A Syst. Hum., vol. 3, no. 2, pp. 206-222, 2003. [http://dx.doi.org/10.1109/TSMCA.2003.811290].
[6]
I. Kononenko, "Machine learning for medical diagnosis: history, state of the art and perspective", Artif. Intell. Med., vol. 23, no. 1, pp. 89-109, 2001. [http://dx.doi.org/10.1016/S0933-3657(01)00077-X]. [PMID: 11470218].
[7]
P. Luukka, "Feature selection using fuzzy entropy measures with similarity classifier", Expert Syst. Appl., vol. 38, no. 4, pp. 4600-4607, 2011. [http://dx.doi.org/10.1016/j.eswa.2010.09.133].
[8]
H. Kahramanli, and N. Allahverd, "Extracting rules for classification problems: AIS based approach", In: Expert Systems with Applications., vol. 36. 2009, no. 7, pp. 10494-10502. [https://doi.org/10.1016/j.eswa.2009.01.029].
[9]
B.D. Sekar, C.D. Ming, J. Shi, and Y.H. Xiang, "Fused hierarchical neural networks for cardiovascular disease diagnosis", IEEE Sens. J., vol. 12, no. 3, pp. 644-650, 2012. [http://dx.doi.org/10.1109/JSEN.2011.2129506].
[10]
M. Seera, and C.P. Lim, "A hybrid intelligent system for medical data classification", Expert Syst. Appl., vol. 41, no. 5, pp. 2239-2249, 2014. [http://dx.doi.org/10.1016/j.eswa.2013.09.022].
[11]
J.P. Garcia-Laencina, J. Luis, S. Gómeza, A.R. Figueiras-Vidal, and M. Verleysen, "K nearest neighbors with mutual information for simultaneous classification and missing data imputation", Neurocomputing, vol. 72, no. 7-9, pp. 1483-1493, 2009. [https://doi.org/10.1016/j.neucom.2008.11.026].
[12]
F. Amato, A. López, E.M. Peña-Méndez, P. Vaňhara, A. Hamp, and J. Havel, "Artificial neural networks in medical diagnosis", J. Appl. Biomed., vol. 11, no. 2, pp. 47-58, 2013. [http://dx.doi.org/10.2478/v10136-012-0031-x].
[13]
M. Brameier, and W. Banzhaf, "A comparison of linear genetic programming and neural networks in medical data mining", IEEE Trans. Evol. Comput., vol. 5, no. 1, pp. 17-26, 2001. [http://dx.doi.org/10.1109/4235.910462].
[14]
J.S. Sartakhti, M.H. Zangooei, and K. Mozafari, "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)", Comput. Methods Prog Biomed., vol. 108, no. 2, pp. 570-579, 2012. [http://dx.doi.org/10.1016/j.cmpb.2011.08.003]. [PMID: 21968203].
[15]
C.Y. Fan, P.C. Chang, J.J. Lin, and J.C. Hsieh, "A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification", Appl. Soft Comput., vol. 11, no. 1, pp. 632-644, 2011. [http://dx.doi.org/10.1016/j.asoc.2009.12.023].
[16]
K.V.S.R.P. Varma, A.A. Rao, T.S.M. Lakshmi, and P.V.N. Rao, "A computational intelligence approach for a better diagnosis of diabetic patients", Comput. Electr. Eng., vol. 40, no. 5, pp. 1758-1765, 2014. [http://dx.doi.org/10.1016/j.compeleceng.2013.07.003].
[17]
R. Cheruku, D.R. Edla, and V. Kuppili, "SM-RuleMiner: spider monkey based rule miner using novel fitness function for diabetes classification", Comput. Biol. Med., vol. 81, pp. 79-92, 2017. [http://dx.doi.org/10.1016/j.compbiomed.2016.12.009]. [PMID: 28027460].
[18]
A.S. Anuradha, and G. Gupta, "ANT_FDCSM: Aanovel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients", J. Intelligent. Fuzzy Syst., vol. 36, no. 1, pp. 747-760, 2019. [http://dx.doi.org/10.3233/JIFS-172240].
[19]
A.S. Anuradha, and G. Gupta, "An intelligent two phase fuzzy decision tree based clustering model for design of computer aided detection/diagnosis (CADe/CADx) system", MAPAN (Springer), vol. 33, no. 1, pp. 63-75, 2017. [https://doi.org/10.1007/s12647-017-0230-8].
[20]
A.S. Anuradha, and G. Gupta, "Fuzzy decision tree construction in crisp scenario through fuzzified trapezoidal membership function", Internetw. Indonesia, vol. 7, no. 2, pp. 21-28, 2015.
[21]
B.Z. Dadaneh, H.Y. Markid, and A. Zakerolhosseini, "Unsupervised probabilistic feature selection using ant colony optimization", Expert Syst. Appl., vol. 53, pp. 27-42, 2016. [http://dx.doi.org/10.1016/j.eswa.2016.01.021].
[22]
H.H. Örkcü, and H. Bal, "Comparing performances of backpropagation and genetic algorithms in the data classification", Expert Syst. Appl., vol. 38, no. 4, pp. 3703-3709, 2011. [http://dx.doi.org/10.1016/j.eswa.2010.09.028].
[23]
K. Polat, S. Şahan, H. Kodaz, and S. Güneş, "Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism", Expert Syst. Appl., vol. 32, no. 1, pp. 172-183, 2007. [http://dx.doi.org/10.1016/j.eswa.2005.11.024].
[24]
R. Stoean, and C. Stoean, "Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection", Expert Syst. Appl., vol. 40, no. 7, pp. 2677-2686, 2013. [http://dx.doi.org/10.1016/j.eswa.2012.11.007].
[25]
E. Çomak, K. Polat, S. Güneş, and A. Arslan, "A new medical decision making system: least square support vector machine (LSSVM) with fuzzy weighting pre-processing", Expert Syst. Appl., vol. 32, no. 2, pp. 409-414, 2007. [http://dx.doi.org/10.1016/j.eswa.2005.12.001].
[26]
J-Z. Guo, and J-W. Jin, "An improved Id3 algorithm for medical data classification", Comput. Electr. Eng., vol. 65, pp. 474-487, 2018. [https://doi.org/10.1016/j.compeleceng.2017.08.005].
[27]
M.B. Gorzałczany, and F. Rudziński, "Interpretable and accurate medical data classification -a multi-objective genetic-fuzzy optimization approach", Expert Syst. Appl., vol. 71, no. 1, pp. 26-39, 2017. [http://dx.doi.org/10.1016/j.eswa.2016.11.017].
[28]
A. Kalantari, A. Kamsin, S. Shamshirband, A. Gani, H. Alinejad-Rokny, and T.C. Anthony, "Computational intelligence approaches for classification of medical data: state-of-the-art, future challenges and research directions", Neurocomputing, vol. 276, no. 7, pp. 2-22, 2018. [http://dx.doi.org/10.1016/j.neucom.2017.01.126].
[29]
S. Al-Muhaideb, and E.B.M. Mohamed, "An individualized preprocessing for medical data classification", Procedia Comput. Sci., vol. 82, pp. 35-42, 2016. [http://dx.doi.org/10.1016/j.procs.2016.04.006].
[30]
P. Mohapatra, S. Chakravarty, and P.K. Dash, An improved cuckoo search based extreme learning machine for medical data classificationSwarm Evol. Comput., vol. 24. no. , pp. 25-49. 2015
[http://dx.doi.org/10.1016/j.swevo.2015.05.003]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 12
ISSUE: 4
Year: 2019
Page: [250 - 259]
Pages: 10
DOI: 10.2174/2213275912666181205155018
Price: $58

Article Metrics

PDF: 34
HTML: 2
PRC: 1