Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Clinical Models CM3aD and CM3bD ................................................................................. 179New Data Mining Process .................................................................................................. 181C.5. Chapter 7 Experiments .................................................................................................... 182C.5.1. Clinical Models CM1 and CM2 ................................................................................ 182C.5.2. Clinical Models ........................................................................................................ 184Models CM3a, CM3b, CM4a, and CM4b ........................................................................... 184C.5.3. Scoring Risk models................................................................................................. 189C.5.4. Clustering Models .................................................................................................... 191Models CM3a and CM3b ................................................................................................... 191C.6. Case Study V .................................................................................................................. 193C.7. Case Study VI ................................................................................................................. 195Model CM3aD ................................................................................................................... 195vi
List of FiguresFig. 3.1 Typical pattern recognition system architecture (Schalkoff, 1992) 27Fig. 3.2 Model for statistical pattern recognition (Jain et al, 2000) 31Fig. 3.3 Using syntactic pattern approach for classification (Schalkoff, 1992) 32Fig. 3.4 A decision tree for the data in Table 3.2 34Fig. 3.5 An example of a linear classification problem 37Fig. 3.6Gaussian distributions of data in a 3-dimensional space38Fig.3.7An example of a nonlinear problem38Fig. 3.8 Classification performance rates 43Fig. 4.1 Node in full perceptron model. 52Fig. 4.2 Example of linear hyper-plane 53Fig. 4.3 An example of a multilayer perceptron 54Fig. 4.4 The example structure of radial basis function networks 56Fig. 4.5 The description of support vectors 58Fig. 4.6 Architecture of support vector machine (Haykin, 1999). 60Fig. 4.7 The description of a self organizing map technique. 62Fig. 4.8 The example of the final U-matrix 63Fig. 4.9 Vote data results in sensitivity, specificity, accuracy and error rates. 73Fig. 5.1Overview of a “knowledge discovery from data” process (Hand et al,2001)78Fig. 5.2 The methodology of CRISP-DM (Shearer, 2000) 80Fig. 5.3 A general thesis frame work; based on (Davis, 2007) 82Fig. 5.4 The detailed steps for the thesis experiments. 88vii
Page 1 and 2: THE UNIVERSITY OF HULLPredicting Ca
Page 3 and 4: 3.4.2. Nonlinear Models ...........
Page 5: 8.3. Mutual Information ...........
Page 9 and 10: List of TablesTable 2.1The 11 facto
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
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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
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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
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Step 5 (Building New Models): The n
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