Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease CAD touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system CDSS for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

[1]  G. Schuler,et al.  Human minimally invasive off-pump valve-in-a-valve implantation. , 2008, The Annals of thoracic surgery.

[2]  Cheeneebas.  Artificial Neural Network as a Clinical Decision-Supporting Tool to Predict Cardiovascular Disease , 2009 .

[3]  William J. Long,et al.  Using Classification Tree and Logistic Regression Methods to Diagnose Myocardial Infarction , 1998, MedInfo.

[4]  S. Aji,et al.  A Neuro Fuzzy Decision Tree Model for Predicting the Risk in Coronary Artery Disease , 2007, 2007 IEEE 22nd International Symposium on Intelligent Control.

[5]  Jeremy C. Wyatt,et al.  9. Decision support systems , 2000 .

[6]  Xiao Han,et al.  A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data , 2012, Knowl. Based Syst..

[7]  Tahseen Ahmed Jilani,et al.  Acute Coronary Syndrome Prediction Using Data Mining Techniques- An Application , 2009 .

[8]  B. Bloom.  Crossing the Quality Chasm: A New Health System for the 21st Century , 2002 .

[9]  Xiaodong Yang,et al.  Multifractal analysis of human synchronous 12-lead ECG signals using multiple scale factors , 2007 .

[10]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[11]  S. Gundry,et al.  Robotically Assisted Cardiac Surgery: Minimally Invasive Techniques to Totally Endoscopic Heart Surgery , 2003, The Journal of cardiovascular nursing.

[12]  Berend Jan van der Zwaag,et al.  Fuzzy logic in clinical practice decision support systems , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[13]  Carlos Ordonez.  Comparing association rules and decision trees for disease prediction , 2006, HIKM '06.

[14]  B Drew,et al.  Comparison of 18-lead ECG and selected body surface potential mapping leads in determining maximally deviated ST lead and efficacy in detecting acute myocardial ischemia during coronary occlusion. , 1999, Journal of electrocardiology.

[15]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[16]  F. Mohr,et al.  Total endoscopic computer enhanced coronary artery bypass grafting. , 2000, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[17]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[18]  Basir Abidin,et al.  Use of fuzzy neural network to predict coronary heart disease in a Malaysian sample , 2009, IEEE ICT 2009.

[19]  Louaï Adhami,et al.  Planning, simulation, and augmented reality for robotic cardiac procedures: The STARS system of the ChIR team. , 2003, Seminars in thoracic and cardiovascular surgery.

[20]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..

[21]  Ève Coste-Manière,et al.  Optimal Planning of Robotically Assisted Heart Surgery: First Results on the Transfer Precision in the Operating Room , 2004, Int. J. Robotics Res..

[22]  Z. Rahimi,et al.  The angiotensin converting enzyme D allele is an independent risk factor for early onset coronary artery disease. , 2010, Clinical biochemistry.

[23]  Krzysztof J. Cios,et al.  Uniqueness of medical data mining , 2002, Artif. Intell. Medicine.

[24]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[25]  L. Papaconstantinou,et al.  Association rule analysis for the assessment of the risk of coronary heart events , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Constantinos S. Pattichis,et al.  Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees , 2010, IEEE Transactions on Information Technology in Biomedicine.

[27]  Randolph A. Miller,et al.  Review: Medical Diagnostic Decision Support Systems - Past, Present, And Future: A Threaded Bibliography and Brief Commentary , 1994, J. Am. Medical Informatics Assoc..

[28]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[29]  H. Koh,et al.  Data mining applications in healthcare. , 2005, Journal of healthcare information management : JHIM.

[30]  Kiran Jyoti,et al.  A Novel Approach for Heart Disease Diagnosis using Data Mining and Fuzzy Logic , 2012 .

[31]  F. Mohr,et al.  Minimally invasive hybrid coronary artery revascularization. , 2008, The Annals of thoracic surgery.

[32]  Adam Wright,et al.  White paper: A Roadmap for National Action on Clinical Decision Support , 2007, J. Am. Medical Informatics Assoc..

[33]  David W. Bates,et al.  Synthesis of Research Paper: Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality , 2003, J. Am. Medical Informatics Assoc..

[34]  Sellappan Palaniappan,et al.  Intelligent heart disease prediction system using data mining techniques , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[35]  S. Nitzsche,et al.  Cardio navigation: planning, simulation, and augmented reality in robotic assisted endoscopic bypass grafting. , 2005, The Annals of thoracic surgery.

[36]  Dimitrios I. Fotiadis,et al.  Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling , 2008, IEEE Transactions on Information Technology in Biomedicine.

[37]  Jafar Habibi,et al.  Diagnosis of Coronary Artery Disease Using Data Mining Techniques Based on Symptoms and ECG Features , 2012 .

[38]  Kensaku Kawamoto,et al.  Development, Deployment and Usability of a Point-of-Care Decision Support System for Chronic Disease Management Using the Recently-Approved HL7 Decision Support Service Standard , 2007, MedInfo.

[39]  Edward H. Shortliffe,et al.  Computer-based medical consultations, MYCIN , 1976 .

[40]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[41]  Michele M Pelter,et al.  Designing prehospital ECG systems for acute coronary syndromes. Lessons learned from clinical trials involving 12-lead ST-segment monitoring. , 2005, Journal of electrocardiology.

[42]  Gholam Ali Montazer,et al.  A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment , 2010, Expert Syst. Appl..

[43]  P. K. Anooj,et al.  Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules , 2012, J. King Saud Univ. Comput. Inf. Sci..

[44]  Constantinos S. Pattichis,et al.  Assessment of the risk of coronary heart event based on data mining , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[45]  Jan Komorowski,et al.  Modelling prognostic power of cardiac tests using rough sets , 1999, Artif. Intell. Medicine.