A Knowledgeable Decision Tree Classification Model for Multivariate Heart Disease Data-A Boon to Healthcare

In a smart hospital, effective decision supports are useful for medical diagnosis. Recent advances in the field of data mining, pervasive computing and other computing methods are ready to meet this kind of challenges. However, few techniques can be gracefully adopted for generating accurate and reliable as well as biologically interpretable rules. The objective of this paper is to introduce a novel method for classifying coronary artery disease dataset based on the principle of decision trees. We extend classical decision tree building algorithms to handle data sets with Multivariate in nature. Extensive experiments have been conducted which shows that the resulting classifiers are more accurate than the existing classifiers. The performance of the algorithm is evaluated with the coronary artery disease (CAD) data sets taken from University California Irvine (UCI).

[1]  G. NaliniPriya,et al.  Neural network based efficient knowledge discovery in hospital databases using RFID technology , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[2]  K. Preston White,et al.  Using RFID Technologies to Capture Simulation Data in a Hospital Emergency Department , 2006, Proceedings of the 2006 Winter Simulation Conference.

[3]  Enrico W. Coiera,et al.  Interruptive communication patterns in the intensive care unit ward round , 2005, Int. J. Medical Informatics.

[4]  Hichem Sahbi,et al.  Context-Dependent Kernels for Object Classification , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Jian-Bo Yang,et al.  Determination of Global Minima of Some Common Validation Functions in Support Vector Machine , 2011, IEEE Transactions on Neural Networks.

[6]  Jin-Mao Wei,et al.  Ensemble Rough Hypercuboid Approach for Classifying Cancers , 2010, IEEE Transactions on Knowledge and Data Engineering.

[7]  Moncef Gabbouj,et al.  Evaluation of Global and Local Training Techniques over Feed-forward Neural Network Architecture Spaces for Computer-aided Medical Diagnosis , 2022 .

[8]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[9]  Huanhuan Chen,et al.  Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[10]  Abdulkadir Sengür,et al.  Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..

[11]  Zhaohong Deng,et al.  Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[12]  Bhavani M. Thuraisingham,et al.  Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints , 2011, IEEE Transactions on Knowledge and Data Engineering.

[13]  Rong Jin,et al.  Efficient Algorithm for Localized Support Vector Machine , 2010, IEEE Transactions on Knowledge and Data Engineering.

[14]  Pedro Antonio Gutiérrez,et al.  Logistic Regression by Means of Evolutionary Radial Basis Function Neural Networks , 2011, IEEE Transactions on Neural Networks.

[15]  Bin Wang,et al.  ELITE: Ensemble of Optimal Input-Pruned Neural Networks Using TRUST-TECH , 2011, IEEE Transactions on Neural Networks.

[16]  Zhengxin Chen,et al.  Privacy-Preserving Data Mining of Medical Data Using Data Separation-Based Techniques , 2007, Data Sci. J..

[17]  Shehroz S. Khan,et al.  Computation of Initial Modes for K-modes Clustering Algorithm Using Evidence Accumulation , 2007, IJCAI.

[18]  Sau Dan Lee,et al.  Decision Trees for Uncertain Data , 2011, IEEE Trans. Knowl. Data Eng..

[19]  Noor Akhmad Setiawan,et al.  Rule Selection for Coronary Artery Disease Diagnosis Based on Rough Set , 2009 .

[20]  Terry Windeatt,et al.  Embedded Feature Ranking for Ensemble MLP Classifiers , 2011, IEEE Transactions on Neural Networks.