Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

  • B. Trstenjak Department of Computer Engineering, Polytechnic of Medimurje in Cakovec, Croatia
  • D. Donko Department of Computer ScienceFaculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina
  • Z. Avdagic Department of Computer ScienceFaculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina
Keywords: disease prediction, web framework, hybrid model, Case Based Reasoning

Abstract

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.

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References

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

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

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

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

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

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

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

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

M. M. Richter, R. Weber, “Case-Based Reasoning: A Textbook”, in Basic CBR Elements, Springer Science & Business Media, pp. 17-34, 2013

A. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, Vol. 31, No. 8, p. 651–666, 2010

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

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

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

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

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

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

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