Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model
Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.
Keywords:disease prediction, web framework, hybrid model, Case Based Reasoning
F. Amato, A. Lopez, E. M. Pena-Mendez, P. Vanhara, A. Hampl, J. Havel, “Artificial neural networks in medical diagnosis”, Journal of Applied Biomedicine, Vol. 11, pp. 47-58, 2013 DOI: https://doi.org/10.2478/v10136-012-0031-x
C. C. Sady, A. L. P. Ribeiro, “Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease”, Computers in Biology and Medicine, Vol. 70, pp. 220-227, 2016 DOI: https://doi.org/10.1016/j.compbiomed.2016.01.016
M. -C. Yang, C. -S. Huang, J. -H. Chen, R.-F. Chang, “Whole Breast Lesion Detection Using Naive Bayes Classifier for Portable Ultrasound”, Ultrasound in Medicine & Biology, Vol. 38, No. 11, pp. 1870–1880, 2012 DOI: https://doi.org/10.1016/j.ultrasmedbio.2012.07.006
S. Bashir, U. Qamar, F. H. Khan, L. Naseem, “HMV: A medical decision support framework using multi-layerclassifiers for disease prediction”, Journal of Computational Science, Vol. 13, pp. 10-25, 2016 DOI: https://doi.org/10.1016/j.jocs.2016.01.001
K. Sudhakar, D. M. Manimekalai, “Propose a Enhanced Framework for Prediction of Heart Disease”, Int. Journal of Engineering Research and Applications, Vol. 5, No. 4, pp. 1-6, 2015 DOI: https://doi.org/10.17577/IJERTV4IS040476
S. Bashir, U. Qamar, F. H. Khan, M. Y. Javed, “MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble”, Arabian Journal for Science and Engineering, Vol. 39, No. 11, pp. 7771-7783, 2014 DOI: https://doi.org/10.1007/s13369-014-1315-0
R. Prashantha, S. D. Roya, P. K. Mandal, S. Ghosh, "Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging”, Expert Systems with Applications, Vol. 41, No. 7, pp. 3333-3342, 2014.
D. S. Medhekar, M. P. Bote , S. D. Deshmukh, “Heart Disease Prediction System using Naive Bayes”, International Journal of Enhaced Research in Science Technology & Engineering, Vol. 2, No. 3, pp. 1-5, 2013
P. K. N. Anooj, “Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules”, Central European Journal of Computer Science, Vol. 1, No. 4, pp. 452-498, 2011 DOI: https://doi.org/10.2478/s13537-011-0032-y
E. M. F. El Houby, “A Framework for Prediction of Response to HCV Therapy Using Different Data Mining Techniques”, Advances in Bioinformatics, Vol. 2014, Article ID 181056, pp. 1-10, 2014 DOI: https://doi.org/10.1155/2014/181056
M. M. Richter, R. Weber, “Case-Based Reasoning: A Textbook”, in Basic CBR Elements, Springer Science & Business Media, pp. 17-34, 2013 DOI: https://doi.org/10.1007/978-3-642-40167-1_2
A. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, Vol. 31, No. 8, p. 651–666, 2010 DOI: https://doi.org/10.1016/j.patrec.2009.09.011
V. R. Patel, R. G. Mehta, “Data Clustering: Integrating Different Distance Measures with Modified k-Means Algorithm”, Advances in Intelligent and Soft Computing, Vol. 131, pp. 691-700, 2012 DOI: https://doi.org/10.1007/978-81-322-0491-6_63
B. Azhagusundari, A. S. Thanamani, “Feature Selection based on Information Gain”, International Journal of Innovative Technology and Exploring Engineering, Vol. 2, No. 2, pp. 18-21, 2013
F. Li, Y. Piao, M. Li, M. Piao, K. Ryu, “Positive impression of low-ranking microrn as in human cancer classification”, in 4th International conference on Computer Science & Information Technology, Sydney, Australia, 2014 DOI: https://doi.org/10.5121/csit.2014.4239
R. Priyadarsini, M. Valarmathi, S. Sivakumari, “Gain Ration based feature selection method for privacy preservation”, ICTACT Journal on Soft Computing, Vol. 1, No. 4, pp. 201-205, 2011
R. Tiwari, M. P. Singh, “Correlation-based Attribute Selection using Genetic Algorithm”, International Journal of Computer Applications, Vol. 4, No. 8, pp. 28-34, 2010 DOI: https://doi.org/10.5120/847-1182
C. L. Blake, C. J. Merz, UCI Repository of machine learning, University of California, Department of Information and Computer Science, 1998
R. Konig, U. Johansson, T. Lofstrom, L. Niklasson, “Improving GP Classification Performance by Injection of Decision Trees”, WCCI 2010 IEEE World Congress on Computational Intelligence, Barcelona, Spain, 2010 DOI: https://doi.org/10.1109/CEC.2010.5585988
L. Jiang, C. Li, “Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination”, Journal of Computers, Vol. 6, No. 7, pp. 1325-1331, 2011 DOI: https://doi.org/10.4304/jcp.6.7.1325-1331
R. G. Ramani, L. Balasubramanian, A. A. Meenal, “A hybrid classification model employing Genetic algorithm and Root Guided Decision Tree for improved categorization of data”, ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 21, pp. 9968-9975, 2015
P. Lakshmi, S. S. Kumar, A. Suresh, “A Novel Hybrid Medical Diagnosis System Based on Genetic Data Adaptation Decision Tree and Clustering”, ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 16, pp. 7293-7299, 2015
C. V. Subbulakshmi, S. N. Deepa, “Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier”, The Scientific World Journal, Vol. 2015, Article ID 418060, pp. 1-12, 2015 DOI: https://doi.org/10.1155/2015/418060
How to Cite
MetricsAbstract Views: 535
PDF Downloads: 252 Data sets used in research Downloads: 0 CBR data models and case datasets Downloads: 0
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.