Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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The results are then divided to different groups in the range from 0%-100%. For each group a predictedmean is calculated in order to calculate the number of predicted “mortality” or “death rate” (see inTables C2, and C3 below).Range of Mean predicted risk No of Predicted Reportedpredicted rate of Mortality (%) operations deaths deathsThe ratio0-10% 3.00% 438 13 60 4.6210-20% 13.48% 39 5 9 1.8020-30% 23.25% 13 3 3 1.0030-40% 32.27% 5 2 4 2.0040-50% 44.86% 3 1 2 2.00>50% 58.37% 1 1 - 0.000-100% 5% 498 25 78 3.12Table C3: Comparison of observed and predicted death from PPOSSUM logistic equations.Step 4 (Comparison/ Evaluation): The comparisons are fulfilled based on the ratios between thepredicted and actual rates. For example, the band group of 20%-30% in Table C2 shows that, thepredicted mortality is calculated based on the mean (24.97%) and the number of operation cases (44).Therefore, the ratio between the reported mortality (8) and the predicted one (11) is 0.73.C.2. Case study IIClinical Model CM3aDStep 1 (Selection): The data is taken from the Dundee site with a selection of 18 attributes (16 inputattributes, and 2 attributes are for the outcome calculations) and 341 patients.Step 2 (Clean/Transform/Filter): The method used here is followed the methods indicated in “DataPreparation Strategy” section in Chapter 5. The detail as follows:Cleaning task: The summary for this task can be seen in Table C4 below. For example, thenumber of missing values for attribute namely “OP DURATION” is 72. The values are in therange of [0.7, 3]. Therefore, missing values will be filled by the mean of non-missing values(1.50). The number of missing values is 10 in the attribute “PATCH”, and the most frequencyvalue of “PTFE” is 170. Therefore, attribute missing values will be filled as “PTFE”.Transformation task: All data is required to transform to numerical values. Therefore,continuous values are rescaled to the values in the range of [0,1] by using normalisationmethod. Boolean values are transformed to values of 0 or 1 respectively. Categorical values are171
172transformed as discrete-Boolean categorical values. They are then treated as Booleantransformation. The transformation summary can be seen in Table C5 below.Attribute NameNumberMean/MaxAttribute valuesof Missingfreq valuesOP DURATION 72 [0.7,3] 1.50ASA 37 [1,4] 2.45EBL 243 [0,2000] 214.18SHUNT FOR CEA 5 Yes/No 213 (No)PATCH 10 None/Other/PTFE/Vein 170 (PTFE)R1-A SIDE 0 Left/RightAGE 0 [42,86] 68Sex 0 Male/Female 215(Male)HYPERTENSIONHX6 None/Yes 176(None)RENAL HX 6 Normal/Abnormal 324 (Normal)RESPIRATORYDIS HX14 Normal/Mild COAD/Mod COAD/sev COAD 280 (Normal)CABG 8 No/Yes 314 (no)ECG 16 Normal/A-Fib
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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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ACCRand = PRand(true positive true
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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
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Page 153 and 154: Adding attribute weights to the clu
Page 155 and 156: system. Moreover, in POSSUM and PPO
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Page 159 and 160: eing located in alternative risk ba
Page 162 and 163: and computer scientists, to verify
Page 164 and 165: Baxt, W. G. (1992). Use of an Artif
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 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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