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Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Volume 13, Number 4, July 2017 - Paper 2 - pp. 348-361
DOI: 10.23940/ijpe.17.04.p2.348361

Sakshi Takkar and Aman Singh

Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

(Submitted on March 17, 2017; First Revised on April 22, 2017; Second Revised on May 22, 2017; Accepted on May 24, 2017)


Liver illness is the most hazardous ailment that influences a large number of individuals consistently and ends man's life. An effective diagnosis model is required in the process of liver disease treatment. This study accordingly aims to employ Case-Based Reasoning (CBR) methodology supported by Genetic Algorithm (GA) to optimize the prediction results of liver disease and to analyze their performances on different datasets. CBR methodology has been implemented to find the prediction results of liver disease for different datasets. We proposed a GA-based CBR framework to compare its performance with CBR in order to observe how effective it is at predicting liver illness. CBR prediction accuracy is very low so it is not very much appreciated. The proposed GA-CBR integrated model outperforms the CBR model by achieving better accuracy for all used datasets of liver disease. In this optimization of weights of features and selection of suitable instances are done simultaneously rather than separately. This leads to better prediction performance as compare to independent models. The outcome of this model illustrates that performance of usual CBR enhances fundamentally by utilizing our integrated GA-CBR model approach.


References: 41

1.     Branch A, Azad I. “Using algorithms to predict liver disease Classification,” Electronics Information & Planning, vol. 3, pp. 255-259, 2015.
2.     Pandey B, Singh A. “Intelligent techniques and applications in liver disorders : A survey,” Int. J. of Biomedical Engineering and Technology, vol. 16, no. 1, pp. 27-70, 2014.
3.     Aamodt A. “Case-Based Reasoning : Foundational Issues , Methodological Variations , and System Approaches,”AI Communications, vol. 7, no. 1,  pp. 39–59, 1994.
4.     Ghaheri A, Shoar S, Naderan M, Hoseini SS. “The Applications of Genetic Algorithms in Medicine,” Oman Medical Journal, vol. 30, no. 6, pp. 406–416, 2015.
5.     Widodo A, Yang B-S. “Support vector machine in machine condition monitoring and fault diagnosis,” Mech. Syst. Signal Process., vol. 21, no. 6, pp. 2560–2574, 2007.
6.     Rodríguez S. “Case-based reasoning as a decision support system for cancer diagnosis : A case study,”  International Journal of Hybrid Intelligent Systems, vol. 6, no. 2, pp. 97-110, 2009.
7.     Lin R. “An intelligent model for liver disease diagnosis,” Artificial Intelligence in Medicine, vol. 47, no. 1, pp. 53-62, 2009.
8.     Sch V. “A Case-based Decision Support System for Individual Stress Diagnosis using Fuzzy Similarity Matching,” Computational Intelligence, vol. 25, no. 3, pp. 180-195, 2009.
9.     Gu D, Liang C, Li X. “Intelligent Technique for Knowledge Reuse of Dental Medical Records Based on Case-Based Reasoning,”Journal of Medical Systems,vol. 34, no. 2, pp. 213–222, 2010.
10.     Lin R, Chuang C. “A hybrid diagnosis model for determining the types of the liver disease,” Comput. Biol. Med., vol. 40, no. 7, pp. 665–670, 2010.
11.     Chuang C. “Case-based reasoning support for liver disease diagnosis,” Artificial  Inteligence in Medicine., vol. 53, no. 1, pp. 15–23, 2011.
12.     Petrovic S, Mishra N, Sundar S. “A novel case based reasoning approach to radiotherapy planning,” Expert Syst. Appl., vol. 38, no. 9, pp. 10759–10769, 2011.
13.     Ekong VE, Ekong VE, Inyang UG, Onibere EA. “Intelligent Decision Support for Depression diagnosis based on Neuro-fuzzy CBR Hybrid Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid,”Modern Applied Science, vol. 6, no. 7, pp. 79- 88, 2015.
14.     Sharaf-el-deen DA, Ibrahim F. “A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems,” Journal of Medical Systems, vol. 38, no. 9, pp. 1-11, 2014.
15.     Yin Z, Dong Z, Lu X, Yu S, Chen X, Duan H. “A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning,” The Journal of Headache and Pain, vol. 16, no. 29, pp. 1-9, 2015.
16.     Singh P. “ACS : Asthma Care Services with the Help of Case Base Reasoning Technique,” inProcedia Computer Science., vol. 48, pp. 561–567, 2015.
17.     Khussainova G, Petrovic S, Jagannathan R. “Retrieval with Clustering in a Case-Based Reasoning System for Radiotherapy Treatment Planning,”Journal of Physics, vol. 012013, pp. 1-11, 2015.
18.     Saraiva RM, Bezerra J, Perkusich M, Almeida H, Siebra C. “A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis : A Pilot Study,”Studies in health technology and informatics, vol. 216, pp. 862–866, 2015.
19.     Banerjee S, Roy A. “Case Based Reasoning in the Detection of Retinal Abnormalities using Decision Trees,” Procedia Comput. Sci., vol. 46, pp. 402–408, 2015.
20.     Chattopadhyay S, Banerjee S, Rabhi FA, Acharya UR. “ A Case- Based Reasoning System for Complex Medical Diagnosis,” Expert Systems, vol. 30, no. 1, pp. 12–20, 2012.
21.     Kim S, Shim JH. “Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry,” Canadian Journal of Civil Engineering, vol. 41, no. 1, pp. 65–73, 2014.
22.     Vinterbo S, Ohno-machado L. “A genetic algorithm approach to multi-disorder diagnosis,” Artificial Intelligence in Medicine, vol. 18, no. 2, pp. 117–132, 2000.
23.     Tan KC, Yu Q, Heng CM, Lee TH. “Evolutionary computing for knowledge discovery in medical diagnosis,”Artificial Intelligence in Medicine, vol. 27, no. 2, pp. 129–154, 2003.
24.     Zhang Y, Rockett PI. “A generic optimising feature extraction method using multiobjective genetic programming,” Applied Soft Computing, vol. 11, no. 1,  pp. 1087–1097, 2011.
25.     Wu C, Lee W, Chen Y, Lai C, Hsieh K. “Expert Systems with Applications Ultrasonic liver tissue characterization by feature fusion,” Expert System Applications, vol. 39, no. 10, pp. 9389–9397, 2012.
26.     Pal P, Tomar S, Singh R. “Evolutionary Continuous Genetic Algorithm for Clinical Decision Support System,” African Journal of Computing & ICT, vol. 6, no. 1, pp. 127–140, 2013.
27.     Karegowda AG, Manjunath AS, Jayaram MA. “Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of PIMA Indian Diabetes,” Int. Journal of Soft Computing,vol. 2, no. 2, pp. 15–23, 2011.
28.     Sreedevi E, Padmavathamma PM. “A Threshold Genetic Algorithm for Diagnosis of Diabetes using Minkowski Distance Method,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 7, pp. 5596–5601, 2015.
29.     Meth JMD, Mg A, Ow S, Mo O, Awonusi O. “Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis,” Journal of Medical Diagnosis Methods, vol. 5, no. 1, pp. 1–10, 2016.
30.     Antony DA, Singh G. “Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis,”I.J. Intelligent Systems and Applications, vol. 1, pp. 67–73, 2016.
31.     Ahn H, Kim K, Han I. “Hybrid genetic algorithms and case-basedreasoning systems for customer classification,”Expert Systems, vol. 23, no. 3, pp. 127–144, 2006.
32.     Ahn H, Kim K. “Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach,” Applied Soft Computing, vol. 9, no. 2, pp. 599–607, 2009.
33.     Chang P, Lai C, Lai KR. “A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler’s returning book forecasting,” Decision Support Systems, vol. 42, no. 3, pp. 1715–1729, 2006.
34.     Juan Y, Shih S, Perng Y. “Decision support for housing customization : A hybrid approach using case-based reasoning and genetic algorithm,” Expert Systems with Applications, vol. 31, no. 1, pp. 83–93, 2006.
35.     Park Y, Chun S, Kim B. “Artificial Intelligence in Medicine Cost-sensitive case-based reasoning using a genetic algorithm : Application to medical diagnosis,” Artif. Intell. Med., vol. 51, no. 2, pp. 133–145, 2011.
36.     Watson I, “Case-based reasoning is a methodology not a technology,” Knowledge-Based Syst., vol. 12, no. 5–6, pp. 303–308, 1999.
37.     Kaindl H, Śmiałek M, Nowakowski W. C.“Case-based reuse with partial requirements specifications,” in Proceedings of the 2010 18th IEEE International Requirements Engineering Conference, RE2010, 2010, pp. 399–400.
38.     Mitra R, Basak J. “Methods of case adaptation: A survey,” International Journal of Intelligent Systems, vol. 20, no. 6, pp. 627–645, 2005.
39.     Kumar M, Husian M, Upreti N, Gupta D. “Genetic Algorithm: Review and Application,” Int. J. Inf. Technol. Knowl. Manag., vol. 2, no. 2, pp. 451–454, 2010.
40.     Boral S, Chakraborty S, “A case-based reasoning approach for non-traditional machining processes selection,”Advances in Production Engineering & Management, vol. 11, no. 4, pp. 311–323, 2016.
41.     Khemani D, Selvamani RB, Dhar AR, Michael SM. “InfoFrax : CBR in Fused Cast Refractory Manufacture,” Proc. 6th Eur. Conf. CBR, vol. 2416,  pp. 275–283, 2002.


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