Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Fig.6.1 Alternative epochs and learn rates applied to CM3aD model. 116Fig. 6.2 The final U-matrix of data set for model CM3bD 124Fig. 6.3 The component planes for the attributes in CM3bD model 125Fig.6.4 The clustering result for the map in Figure 6.2 126Fig. 6.5 The labeled risks for the map in Figure 6.4 127Fig. 7.1 The comparisons of accuracy rates over all classifiers. 147Fig. 8.1 Pseudo code of original relief algorithm (Kira and Rendell, 1992) 160Fig. 8.2 A comparison of mutual information and Relief for the CM3a model 169Fig. 8.3 A comparison of mutual information and Relief for the CM2 model 169Fig. C1 The U-matrix and each component plane for model CM3bD 222Fig. C2 The U-matrix for model CM3bD 223Fig. C3 The clustering results of self organizing map Kmeans algorithm 223Fig. C4 A comparison of MI and Relief with CM3aD model 241Fig. C5 A comparison of MI and Relief with CM2 model 242viii
List of TablesTable 2.1The 11 factors used in the INDANA trial (Pocock et al, 2001) withexample values.12Table 2.2 Physiological Score (Copeland et al, 1991) 15Table 2.3 Operative Severity Score (Copeland et al, 1991) 16Table 2.4 An example of PS score calculations 16Table 2.5 An example of OS score calculations 17Table 2.6Table 2.7An example of POSSUM and PPOSSUM calculation using PS andOS scoresComparison of observed and predicted death from POSSUMlogistic equations1920Table 3.1 Pattern recognition (Jain et al, 2000) 28Table 3.2 Information on patients in the cardiovascular domain 29Table 3.3 Comparing statistical pattern recognition, syntactic patternrecognition, and neural network approaches (Schalkoff, 1992)35Table 3.4 Comparisons of linear and nonlinear models 39Table 3.5 Confusion matrix 42Table 3.6 An example of confusion matrix and measurements 45Table 4.1 Some inner-product kernels (Haykin, 1999) 59Table 4.2 The experiment results with Small Soybean Data. 70Table 4.3 10 test results of Zoo data set in randomisation. 71Table 4.4 Results of 10 executions of Votes recording data set 72Table 4.5 Publication comparisons 73Table 5.1 The frequencies of significant attributes in the Hull site data 85ix
Page 1 and 2: THE UNIVERSITY OF HULLPredicting Ca
Page 3 and 4: 3.4.2. Nonlinear Models ...........
Page 5 and 6: 8.3. Mutual Information ...........
Page 7: List of FiguresFig. 3.1 Typical pat
Page 11 and 12: Table 6.14 Alternative number of cr
Page 13 and 14: Table C12 Neural network results of
Page 15 and 16: AbbreviationsNN(s): Neural Network(
Page 17 and 18: Chapter 1 IntroductionThis thesis p
Page 19 and 20: on. According to Bishop (1995), the
Page 21 and 22: techniques is given. The standard c
Page 23 and 24: A knowledge base that stores the in
Page 25 and 26: This score is then compared to the
Page 27 and 28: Cardiac signsRespiratory signsSysto
Page 29 and 30: Given the ratings in Table 2.2, and
Page 31 and 32: R11e3 z2Reg.No PATIENT_STATUS Physi
Page 33 and 34: designed to their specific purposes
Page 35 and 36: Chapter 3 Pattern Recognition3.1. I
Page 37 and 38: algorithms depending on the request
Page 39 and 40: a new patient. For example, t 5 can
Page 41 and 42: And the strategy for pattern learni
Page 43 and 44: Hence, whenever there is a specific
Page 45 and 46: 3.4.2. Nonlinear ModelsDefinition:
Page 47 and 48: vector machine are nonlinear models
Page 49 and 50: From the confusion matrix in Table
Page 51 and 52: the accuracy of 0.90 shows the trad
Page 53 and 54: The comparison between neural netwo
Page 55 and 56: Chapter 4 Supervised and Unsupervis
Page 57 and 58: 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
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MethodA self organizing map tool is
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The SOM algorithm is applied to the
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clustering models CM3aDC and CM3bDC
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clustering results, resulting from
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and CM3bD (KMIX and SOM) suggest th
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equivalent rates for “High risk
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25 input attributes to 16 input att
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7.2.4. Scoring Risk ModelsThis sect
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increasing number of patterns impro
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other words, there are negligible o
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ACCRand = PRand(true positive true
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Therefore, the poor clustering perf
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difficulty of measuring influential
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“High risk” predictions in "cli
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chosen by the user. The methods for
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average information content of the
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MI(X , Y) H(X ) H(X | Y) pi,j( x,
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number of patterns in class C i ; a
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The results in Figure 8.3 show that
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and the prediction risks reflects i
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Adding attribute weights to the clu
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system. Moreover, in POSSUM and PPO
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in scientific and industrial applic
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eing located in alternative risk ba
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and computer scientists, to verify
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Baxt, W. G. (1992). Use of an Artif
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Cristianini, N., Shawe-Taylor, J. (
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Hawkins, R.G., Houston, M.C., Ferra
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Kohonen, T. (1981). Self-organized
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Manning, C.D., Raghvan, P., and Sch
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Ohn, M. S., Van-Nam, H., and Yoshit
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Simelius, K., Stenroos, M., Reinhar
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Wang, K. Wang, L. Wang, D. and Xu,
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Appendix A. Data structureA.1. Hull
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A.2. Dundee siteThe following table
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B.2. Scoring Risk ModelsTable B2 sh
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The results are then divided to dif
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Attribute NameOriginal data Transfo
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AttributeName175Attribute TypeMissi
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Filtering task: The expected outcom
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Data is clustered by SOM Kmeans alg
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197 values of “C2H”. The model
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PATIENT_STATUS Boolean 0 Alive/Dead
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Cleaning and Transformation tasks:
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High riskLow riskCM3a-MLPCM3a-RBFCM
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Table C19: Experimental results of
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Therefore, the Mortality has got 25
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Both models CM3aC and CM3bC are use
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R1 PATECGAMBUL_STATUR1-A SIDEANTI_A
Page 212:
Step 5 (Building New Models): The n
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