Knowledge mining from clinical datasets using rough sets and backpropagation neural network

Computational and Mathematical Methods in Medicine

Volumn 2015, Issue , 2015, Pages

  Nahato, Kindie Biredagn ^a   Harichandran, Khanna Nehemiah ^a   Arputharaj, Kannan ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; CARDIOLOGY; CLASSIFICATION (OF INFORMATION); CLINICAL RESEARCH; DECISION MAKING; DISEASES; LARGE DATASET; NEURAL NETWORKS; ROUGH SET THEORY;

BACK PROPAGATION NEURAL NETWORKS; BREAST CANCER; CLASSIFICATION MODELS; INDISCERNIBILITY RELATION; KNOWLEDGE MINING; MACHINE LEARNING REPOSITORY; MISSING VALUES; UNIVERSITY OF CALIFORNIA;

DATA MINING;

ALBUMIN; ALKALINE PHOSPHATASE; ANTIVIRUS AGENT; ASPARTATE AMINOTRANSFERASE; BILIRUBIN; STEROID;

ADULT; AGED; ANOREXIA; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BREAST CANCER; CELL ADHESION; CELL SHAPE; CELL SIZE; CLASSIFICATION; CLINICAL DATA REPOSITORY; CLINICAL DECISION MAKING; CONTROLLED STUDY; DATA ANALYSIS; DATA MINING; EPITHELIUM CELL; EVALUATION STUDY; FATIGUE; FEMALE; FUZZY SYSTEM; HEART DISEASE; HEPATITIS; HUMAN; INFORMATION SYSTEM; KNOWLEDGE DISCOVERY; LEARNING ALGORITHM; LIVER SIZE; MAJOR CLINICAL STUDY; MALAISE; MALE; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; MITOSIS; ROUGH SET; SUPPORT VECTOR MACHINE; THICKNESS; ALGORITHM; BREAST NEOPLASMS; COMPUTER SIMULATION; DECISION MAKING; FACTUAL DATABASE; FUZZY LOGIC; HEART DISEASES; INFORMATION PROCESSING; MACHINE LEARNING; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; UNITED STATES;

ALGORITHMS; BREAST NEOPLASMS; COMPUTER SIMULATION; DATA COLLECTION; DATA MINING; DATABASES, FACTUAL; DECISION MAKING; FEMALE; FUZZY LOGIC; HEART DISEASES; HEPATITIS; HUMANS; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); ROC CURVE; SUPPORT VECTOR MACHINE; UNITED STATES;

EID: 84925308660     PISSN: 1748670X     EISSN: 17486718     Source Type: Journal    
DOI: 10.1155/2015/460189     Document Type: Article

References (28)

1. J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2011.
2. M.-S. Chen, J. Han, and P. S. Yu, "Data mining: an overview froma database perspective, " IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, 1996.
3. S. H. El-Sappagh and S. El-Masri, "A distributed clinical decision support system architecture, " Journal of King Saud University-Computer and Information Sciences, vol. 26, no. 1, pp. 69-78, 2014.
4. I. M. Mullins, M. S. Siadaty, J. Lyman et al., "Data mining and clinical data repositories: insights from a 667, 000 patient data set, " Computers in Biology andMedicine, vol. 36, no. 12, pp. 1351-1377, 2006.
5. R. Agrawal, C. Faloutsos, and A. Swami, "Efficient similarity search in sequence databases, " in Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, October 1993.
6. P. Giudici, Applied Data Mining Statistical Methods for Business and Industry, JohnWiley and Sons, Chichester, UK, 2003.
7. Z. Pawlak and A. Skowron, "Rudiments of rough sets, " Information Sciences, vol. 177, no. 1, pp. 3-27, 2007.
8. Z. Pawlak, "Rough set approach to knowledge-based decision support, " European Journal of Operational Research, vol. 99, no. 1, pp. 48-57, 1997.
9. Z. Pawlak, "Rough sets, " International Journal of Computer and Information Sciences, vol. 11, no. 5, pp. 341-356, 1982.
10. S. N. Sivanandam and S. N. Deepa, Principles of Soft Computing, Wiley, New Delhi, India, 2nd edition, 2012.
11. J.-D. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence, PHI Learning, New Delhi, India, 2011.
12. S. Rajesekaran and V. Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications, Learning Private Limited, New Delhi, India, 2011.
13. K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2013.
14. Y. E. Shao, C.-D. Hou, and C.-C. Chiu, "Hybrid intelligent modeling schemes for heart disease classification, " Applied Soft Computing Journal, vol. 14, pp. 47-52, 2014.
15. Y. Kaya and M. Uyar, "A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease, " Applied Soft Computing Journal, vol. 13, no. 8, pp. 3429-3438, 2013.
16. J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, "Hepatitis disease diagnosis using a novel hybridmethod based on support vector machine and simulated annealing (SVM-SA), " Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 570-579, 2012.
17. P. K. Anooj, "Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules, " Journal of King Saud University-Computer and Information Sciences, vol. 24, no. 1, pp. 27-40, 2012.
18. H.-L. Chen, B. Yang, J. Liu, and D.-Y. Liu, "A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis, " Expert Systems with Applications, vol. 38, no. 7, pp. 9014-9022, 2011.
19. A. Marcano-Cedeño, J. Quintanilla-Domínguez, and D. Andina, "WBCD breast cancer database classification applying artificial metaplasticity neural network, " Expert Systems with Applications, vol. 38, no. 8, pp. 9573-9579, 2011.
20. H.-L. Chen, D.-Y. Liu, B. Yang, J. Liu, and G. Wang, "A new hybrid method based on local fisher discriminant analysis and support vectormachines for hepatitis disease diagnosis, " Expert Systems with Applications, vol. 38, no. 9, pp. 11796-11803, 2011.
21. D. Calisir and E. Dogantekin, "A new intelligent hepatitis diagnosis system: PCA-LSSVM, " Expert Systems with Applications, vol. 38, no. 8, pp. 10705-10708, 2011.
22. K. Vijaya, H. K. Nehemiah, A. Kannan, and N. G. Bhuvaneswari, "Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases, " International Journal of Data Mining, Modelling and Management, vol. 2, no. 4, pp. 388-402, 2010.
23. E. Dogantekin, A. Dogantekin, and D. Avci, "Automatic hepatitis diagnosis system based on LinearDiscriminant Analysis and Adaptive Network based on Fuzzy Inference System, " Expert Systems with Applications, vol. 36, no. 8, pp. 11282-11286, 2009.
24. M. Karabatak and M. C. Ince, "An expert system for detection of breast cancer based on association rules and neural network, " Expert Systems with Applications, vol. 36, no. 2, pp. 3465-3469, 2009.
25. H. Kahramanli and N. Allahverdi, "Design of a hybrid system for the diabetes and heart diseases, " Expert Systems with Applications, vol. 35, no. 1-2, pp. 82-89, 2008.
26. M. S. Bascil and F. Temurtas, "A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt training algorithm, " Journal of Medical Systems, vol. 35, no. 3, pp. 433-436, 2011.
27. K. Polat and S. Günes, "Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation, " Digital Signal Processing, vol. 16, no. 6, pp. 889-901, 2006.
28. B. Zheng, S. W. Yoon, and S. S. Lam, "Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vectormachine algorithms, " Expert Systems with Applications, vol. 41, no. 4, pp. 1476-1482, 2014.