Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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B.2. Scoring Risk ModelsTable B2 shows the summary of applying scoring risk models to the thesis data. The outcomes arebased on heuristic rules derived from POSSUM and PPOSSUM threshold scores.ModelsInputnumbersPatternnumbersRisk PredictionUsing ClassifiersMortality 18 499 High risk; Low risk POSSUMMortbidity 18 499 High risk; Low risk POSSUMDeath rate 18 499 High risk; Low risk PPOSSUMTable B2: Scoring risk models summary.B.3. Clustering ModelsTable B3 shows the summary of clustering models used with the thesis data. The outcomes are derivedfrom KMIX and WKMIX clustering algorithms.ModelsInputnumbersPatternnumbersRisk PredictionUsing ClassifiersCM3aDC 16 341 C2H; C1L MLP; SVMCM3bDC 16 341 C3H; C2M; C1L MLP; SVMCM3aC 16 839 C2H; C1L MLP; RBF; SVMCM3bC 16 839 C4VH; C3H; C2M; C1L MLP; RBF; SVMCM3aDC 16 341WKMIX Outcomes (C2H;C1L)MLP; RBF; SVM; J48Table B3: Clustering models summary.169
Appendix C. Experimental Data ExplanationsThis appendix describes the detailed steps of data preparation and experimental explanations for allthesis case studies and the Chapter 8 experiments. These steps are fulfilled following the thesismethodology in Chapter 4 as in the systematic representations.C.1. Case study IStep 1 (Selection): Data is taken from Hull site including 4 attributes and 498 cases (see statisticalanalysis in Table C1). Note that 2 over 4 attributes are the physiological score and operative severescore calculated by POSSUM and PPOSSUM.PSOSNValid 498 498Missing 0 0Mean 20.36 14.30Std. Error of Mean .247 .064Std. Deviation 5.507 1.421Minimum 12 13Maximum 41 23Table C1: Statistical analyses of PS and OS score in the Hull site.Step 2 (Clean/Transform/Filter): All data is cleaned. Therefore, this step is ignored.Step 3 (POSSUM and PPOSSUM Techniques): Data is used with the POSSUM and PPOSSUMformulas (Equations 2.1, 2.2, and 2.3 in Chapter 2) to produce the mortality, morbidity, and death ratescores for individual patients.Range of Mean predicted No of Predicted Reportedpredicted rate risk of Mortality % operations deaths deathsThe ratio0-10% 6.75% 274 19 29 1.5310-20% 14.85% 148 22 28 1.2720-30% 24.97% 44 11 8 0.7330-40% 34.90% 11 4 3 0.7540-50% 43.10% 16 7 7 1.00>50% 60.85% 5 3 3 1.000-100% 13% 498 65 78 1.20170Table C2: Comparison of observed and predicted death from POSSUM logistic equations.
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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
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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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Page 180 and 181: Appendix A. Data structureA.1. Hull
Page 182 and 183: A.2. Dundee siteThe following table
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Page 192 and 193: Filtering task: The expected outcom
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