Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Page 1 and 2:
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
Page 115 and 116: The SOM algorithm is applied to the
Page 117 and 118: clustering models CM3aDC and CM3bDC
Page 119 and 120: clustering results, resulting from
Page 121 and 122: and CM3bD (KMIX and SOM) suggest th
Page 123 and 124: equivalent rates for “High risk
Page 125 and 126: 25 input attributes to 16 input att
Page 127 and 128: 7.2.4. Scoring Risk ModelsThis sect
Page 129 and 130: increasing number of patterns impro
Page 131 and 132: other words, there are negligible o
Page 133 and 134: ACCRand = PRand(true positive true
Page 135 and 136: Therefore, the poor clustering perf
Page 137 and 138: difficulty of measuring influential
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
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
Page 157 and 158: in scientific and industrial applic
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 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 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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