Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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197 values of “C2H”. The model CM3bDC contains 94 values of “C1L”; 149 values of “C2M”; and 98values of “C3H”.C3(Highrisk)C2(Mediumrisk)C1(Lowrisk)High risk 18 17 13Mediumrisk28 22 23Low risk 103 59 58New Data Mining ProcessACC Sen Spec PPV NPV0.42 0.70Table C11: Clustering results for CM3aD model.Two models CM3aDC and CM3bDC are used with alternative supervised techniques (multilayerperceptron, radial basis function, and support vector machine).Classifiers C2H C1L ACC Sen Spec PPV NPV MSECM3aDC-MLP C2H 188 9(MLP16-0-1; 0.3; 1000.95 0.95 0.94 0.96 0.94 0.04epochs)C1L 8 136CM3aDC- RBF C2H 189 8(RBF_c=1) C1L 10 1340.95 0.96 0.93 0.95 0.94 0.03CM3aDC-SVM C2H 185 12(SVM_poly_p=1) C1L 20 1240.91 0.94 0.86 0.90 0.91 0.09Table C12: Neural network results of CM3aDC.0.260.340.62Classifier C3H C2M C1L ACC Sen Spec PPV NPV MSEC3H 97 0 1CM3bDC-MLP(MLP16-0-3; 0.3; C2M 0 148 1 0.96 0.98 0.89 0.96 0.98 0.02100 epochs) C1L 8 2 84CM3bDC-RBF(RBF_c=1)CM3bDC-SVM(SVM_poly_p=1)181C3H 81 7 10C2M 9 135 5C1L 5 7 82C3H 97 0 1C2M 0 149 0C1L 7 2 85Table C13: Neural network results of CM3bDC.0.92 0.94 0.87 0.95 0.85 0.060.97 0.99 0.90 0.96 0.99 0.08Note that the topology and its parameters here are as follows: multilayer perceptron is used with atopology of 16-0-1 (16 input nodes; 0 hidden nodes; 1 output- 2class nodes for CM3aDC), and 16-0-3
(16 input nodes; 0 hidden nodes; 3 output nodes as three level of risks) for CM3bDC, learning rate=0.3, and number of cycles= 100 epochs; radial basis function has number of centre c of 2; andsupport vector machine is used with poly kernel function, and exponent parameter p=2. The 10-foldcross-validation is used. The results can be seen in Tables C12 and C13.C.5. Chapter 7 ExperimentsC.5.1. Clinical Models CM1 and CM2Step 1 (Selection): The data structure here includes 26 attributes (24 inputs and 2 attributes foroutcome heuristic calculations) and 839 patient records. They are the common attributes derived fromthe Hull (498 cases) and the Dundee (341 cases) data sites. The data structure can be seen in Table C14below.Step 2 (Clean/Transform/Filter):Cleaning task: The missing values are filled as the same method above (the mean for thecontinuous attributes) and the mode (for the categorical or Boolean attributes). However, aspecialized heuristic transformation for the missing values in attribute “PATCH” is used. Therewere 253 missing values in both sites where 243 missing values in the Hull site, and 10 missingvalues in the Dundee site. Therefore, missing values are replaced by the mode values of PATCHattribute in the Hull site (“Dacron”) whereas they are replaced by “PTFE” in the Dundee site.Transformation task: The continuous values in data set are also purely transformed into therange [0,1] with the linear transformation method indicated in Chapter 5 (section 5.3.3 - “DataPreparation Strategy”).Filtering task: The final data set contains 25 instead of 26 input attributes after removing the“empty” one (COMP_GROUP attribute has 650/839 missing values). The outcome for modelCM1 is created by the heuristic formula based on “PATIENT_STATUS “ attribute as follows:IF PATIENT_STATUS = “Dead” “High risk”Otherwise, “Low risk”Hence, CM1 has 126 values of “High risk” and 713 values of “Low risk” respectively.182Attribute nameAttributetypeMissingvaluesAttribute valuesMax Freq/Mean
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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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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
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
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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 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 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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