GA_DTNB: A Hybrid Classifier for Medical Data Diagnosis

Recent trends in medical data prediction have become one of the most challenging tasks for the researchers due to its domain specificity, voluminous, and class imbalanced nature. This paper proposed a genetic algorithms (GA)-based hybrid approach by combining decision table (DT) and Naive Bayes (NB) learners. The proposed approach is divided into two phases. In the first phase, feature selection is done by applying GA search. In the second phase, the newly obtained feature subsets are used as input to combined DTNB to enhance the classification performances of medical data sets. In total, 14 real-world medical domain data sets are selected from University of California, Irvine (UCI) machine learning repository, for conducting the experiment. The experimental results demonstrate that GA-based DTNB is an effective hybrid model in undertaking medical data prediction.

[1]  Xian-Ming Huang,et al.  A new application of rough set to ECG recognition , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[2]  Vikrant Bhateja,et al.  An Improved Mammogram Classification Approach Using Back Propagation Neural Network , 2018 .

[3]  Tanveer Syeda-Mahmood.  Role of machine learning in clinical decision support (Presentation Recording) , 2015 .

[4]  Eibe Frank,et al.  Combining Naive Bayes and Decision Tables , 2008, FLAIRS.

[5]  Chee Peng Lim,et al.  A hybrid intelligent system for medical data classification , 2014, Expert Syst. Appl..

[6]  P. Srimani,et al.  Rough Set Approach for Novel Decision Making in Medical Data for Rule Generation and Cost Sensitiveness , 2014 .

[7]  V. Rajinikanth,et al.  Entropy based segmentation of tumor from brain MR images - a study with teaching learning based optimization , 2017, Pattern Recognit. Lett..

[8]  Kavishwar B. Wagholikar,et al.  Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions , 2012, Journal of Medical Systems.

[9]  C. -Z. Ye,et al.  Fuzzy rules to predict degree of malignancy in brain glioma , 2002, Medical and Biological Engineering and Computing.

[10]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[11]  Roshan Joy Martis,et al.  Editorial: Frontiers in development of intelligent applications for medical imaging processing and computer vision , 2017, Comput. Biol. Medicine.

[12]  Chao Tan,et al.  Prediction of Type-2 Diabetes Based on Several Element Levels in Blood and Chemometrics , 2011, Biological Trace Element Research.

[13]  B Harini,et al.  An efficient feature selection method for classification in health care systems using machine learning techniques , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[14]  Novruz Allahverdi,et al.  Design of a hybrid system for the diabetes and heart diseases , 2008, Expert Syst. Appl..

[15]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[16]  A. A. Rao,et al.  A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes. , 2009, The Journal of the Association of Physicians of India.

[17]  G. M. Nasira,et al.  A New Approach for Diagnosis of Diabetes and Prediction of Cancer Using ANFIS , 2014, 2014 World Congress on Computing and Communication Technologies.

[18]  Mei-Hui Wang,et al.  A Fuzzy Expert System for Diabetes Decision Support Application , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .