Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Baxt, W. G. (1992). Use of an Artificial Neural Network for Data Analysis in Clinical DecisionMaking: The Diagnosis of Acute Coronary Occlusion. Neural Computing, Vol. 2.Baxt, W.G., Skora, J. (1996). Prospective Validation of Artificial Neural Network Trained to IdentifyAcute Myocardial Infarction. Lancet. Vol. 347. Pp:12-5.Bayes, T. (1763), "An Essay towards solving a Problem in the Doctrine of Chances. By the late Rev.Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S.",Philosophical Transactions, Giving Some Account of the Present Undertakings, Studies and Laboursof the Ingenious in Many Considerable Parts of the World. Pp: 370–418Bennet, M. (2006). A Risk Score for Cardiovascular Disease. Available online at website:http://www.riskscore.org.uk/.Berkhin, P. (2002). Summary of Clustering Data Mining Techniques, Springer Berlin Heidelberg. Pp:25-71.Bigi, R., Gregori, D., Cortigiani, L., Desideri, A., Chiarotto, F.A., Toffolo, G.M. (2005). ArtificialNeural Networks and Robust Bayesian Classifiers for Risk Stratification Following UncomplicatedMyocardial Infarction. International Journal of Cardiology. Vol.101.(3). Pp: 481-487.Bishop, C. (1995). Neural Network for Pattern Recognition. Oxford University Press.Boser, B., Guyon, I., and Vapnik, V.N. (1992). S Training Algorithm for Optimal Margin Classifiers.Fifth Annual Workshop on Computational Learning Theory. Pp: 144-152.Bramer, M. (2007). Principle of Data Mining. Springer Press.Breiman, L., Freidman, J. H., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees.Wadsworth.Cacciafesta, M., Campana, F., Piccirillo, G., Cicconetti, P., Trani, I., Leonetti-Luparini, R., Marigliano,V., Verico, P. (2001). Neural Network Analysis in Predicting 2-year Survival in Elderly People: ANew Statistical– Mathematical Approach. Gerontology Geriatrics.Vol. 32. Pp: 35–44.Cacciafesta, M., Campana, F., Trani, I., Annichiarico, R., Leonetti-Luparini, R., Gianni, W., Cicconetti,P., Piccirillo, G. (2000). A Neural Network Study of The Correlations between Metabolic-Cardiovascular Diseases and Disability in Elderly People. Gerontology. Geriatrics. Vol. 31.Pp. 257–266.149
Chan, I., Wells, W., Mulkern, R.V, Haker, S., Zhang. J., Zou, K.H., Maier, S.E., (2003). Tempany CM.Detection of Prostate Cancer by Integration of Line-Scan Diffusion, T2-mapping and T2-weightedMagnetic Resonance Imaging; a Multichannel Statistical Classifier. Medical Physics.Vol. 30 (9).Pp:2390-8.Chapman, P., Clinton, J., Khabaza, T., Reinartz, T., and Wirth, R. (1999). The CRSIP –DM ProcessModel. Available online at website: http://www.industrieldatamining.dk/images/crisp-dm.pdf.Cheung, Y-M. (2003). “k*-means: A New Generalized k-means Clustering Algorithm,” PatternRecognition Letters,” Vol. 24. Pp: 2883-2893.Coiera, E. (2003). Guide to Heath Informatics (2nd). Chapter 25. Available online at website:http://www.coiera.com/.Colombet, I., Ruelland, A., Chatellier, G., Gueyffier, F., Degoulet, P., Jaulent, M. C. (2000). Models toPredict Cardiovascular Risk: Comparison of CART, Multilayer perceptron and Logistic Regression.Proceeding of AMIA Symposium. Pp:156-60.Copeland G P, Jones D, Walters M. (1991). POSSUM: A Scoring System for Surgical Audit. BritishJournal Surgery. Vol. 78. Pp: 355-360.Copeland G P. (2002). The POSSUM System of Surgical Audit. Archives of Surgery; Vol.13. Pp: 15-19.Cortes, C., and Vapnik, V.N. (1995). Support Vector Networks. Machine Leanring, Vol.20. Pp: 273-297.COSEHC (2003). Consortium for Southeastern Hypertension Control. Available online at website:http://www.cosehc.org/about.asp.Courant, R., and Hilbert, D. (1970). Methods of Mathematical Physics. Vols. I and II, WileyInterscience, New York.Cover, T. (1965). Geometrical and Statistical Properties of Systems of Linear Inequalities andApplications in Pattern Recognition. IEEE Transaction Electronic. Computing. Vol.14. Pp. 326–334.Cover, T., & Thomas, J. (1991). The Elements of Information Theory. NewYork: Plenum Press.Cox, D.R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society. SeriesB (Methodological), Vol. 34. (2). Pp: 187-220.150
Page 1 and 2:
THE UNIVERSITY OF HULLPredicting Ca
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3.4.2. Nonlinear Models ...........
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8.3. Mutual Information ...........
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List of FiguresFig. 3.1 Typical pat
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List of TablesTable 2.1The 11 facto
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Table 6.14 Alternative number of cr
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Table C12 Neural network results of
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AbbreviationsNN(s): Neural Network(
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Chapter 1 IntroductionThis thesis p
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on. According to Bishop (1995), the
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techniques is given. The standard c
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A knowledge base that stores the in
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This score is then compared to the
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Cardiac signsRespiratory signsSysto
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Given the ratings in Table 2.2, and
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R11e3 z2Reg.No PATIENT_STATUS Physi
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designed to their specific purposes
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Chapter 3 Pattern Recognition3.1. I
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algorithms depending on the request
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a new patient. For example, t 5 can
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And the strategy for pattern learni
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Hence, whenever there is a specific
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3.4.2. Nonlinear ModelsDefinition:
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vector machine are nonlinear models
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From the confusion matrix in Table
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the accuracy of 0.90 shows the trad
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The comparison between neural netwo
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Chapter 4 Supervised and Unsupervis
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Therefore, the visualization of pat
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A popular form of Gaussian basic fu
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mj 1w ( x) b 0jj(4.7)where x is a
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as an instance of the popular K-mea
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o Update the weight as follows:w i
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Discrete (categorical) attributes:
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where d N (X i ,Q j ) is calculated
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Zoo SmallThis data contains 101 cas
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1.20Discussions1.000.800.600.400.20
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Chapter 5 Data Mining Methodology a
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Raw dataDataselectingTarget DataPre
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missing data”, the more detailed
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Step 5 (Comparison/ Evaluation): Th
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Missing values: The data includes 1
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Scoring Risk Models: These are buil
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The missing values for the “Heart
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Data Filtering StageIn this step, t
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Supervised ClassifiersThis section
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5.4. SummaryData mining is a partic
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the significant level of 98.6% (P v
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Attribute nameAttributetypeMissingv
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Attribute nameAttributetypeMissingv
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6.4. Scoring Risk ModelsThe data fo
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are nearly the same (12). The maxim
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inconsistency between the linear an
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epochs can help to reduce the over-
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MSE (0.09). However, the model MLP_
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suitable for the CM3aD data set wit
Page 113 and 114: MethodA self organizing map tool is
Page 115 and 116: The SOM algorithm is applied to the
Page 117 and 118: clustering models CM3aDC and CM3bDC
Page 119 and 120: clustering results, resulting from
Page 121 and 122: and CM3bD (KMIX and SOM) suggest th
Page 123 and 124: equivalent rates for “High risk
Page 125 and 126: 25 input attributes to 16 input att
Page 127 and 128: 7.2.4. Scoring Risk ModelsThis sect
Page 129 and 130: increasing number of patterns impro
Page 131 and 132: other words, there are negligible o
Page 133 and 134: ACCRand = PRand(true positive true
Page 135 and 136: Therefore, the poor clustering perf
Page 137 and 138: difficulty of measuring influential
Page 139 and 140: “High risk” predictions in "cli
Page 141 and 142: chosen by the user. The methods for
Page 143 and 144: average information content of the
Page 145 and 146: MI(X , Y) H(X ) H(X | Y) pi,j( x,
Page 147 and 148: number of patterns in class C i ; a
Page 149 and 150: The results in Figure 8.3 show that
Page 151 and 152: and the prediction risks reflects i
Page 153 and 154: Adding attribute weights to the clu
Page 155 and 156: system. Moreover, in POSSUM and PPO
Page 157 and 158: in scientific and industrial applic
Page 159 and 160: eing located in alternative risk ba
Page 162 and 163: and computer scientists, to verify
Page 166 and 167: Cristianini, N., Shawe-Taylor, J. (
Page 168 and 169: Hawkins, R.G., Houston, M.C., Ferra
Page 170 and 171: Kohonen, T. (1981). Self-organized
Page 172 and 173: Manning, C.D., Raghvan, P., and Sch
Page 174 and 175: Ohn, M. S., Van-Nam, H., and Yoshit
Page 176 and 177: Simelius, K., Stenroos, M., Reinhar
Page 178 and 179: Wang, K. Wang, L. Wang, D. and Xu,
Page 180 and 181: Appendix A. Data structureA.1. Hull
Page 182 and 183: A.2. Dundee siteThe following table
Page 184 and 185: B.2. Scoring Risk ModelsTable B2 sh
Page 186 and 187: The results are then divided to dif
Page 188 and 189: Attribute NameOriginal data Transfo
Page 190 and 191: AttributeName175Attribute TypeMissi
Page 192 and 193: Filtering task: The expected outcom
Page 194 and 195: Data is clustered by SOM Kmeans alg
Page 196 and 197: 197 values of “C2H”. The model
Page 198 and 199: PATIENT_STATUS Boolean 0 Alive/Dead
Page 200 and 201: Cleaning and Transformation tasks:
Page 202 and 203: High riskLow riskCM3a-MLPCM3a-RBFCM
Page 204 and 205: Table C19: Experimental results of
Page 206 and 207: Therefore, the Mortality has got 25
Page 208 and 209: Both models CM3aC and CM3bC are use
Page 210 and 211: R1 PATECGAMBUL_STATUR1-A SIDEANTI_A
Page 212: Step 5 (Building New Models): The n
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