Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Filtering task: The expected outcomes are calculated based on two attributes of “PATIENTSTATUS” and “COMBINE” as the following heuristic rules. Note that the expected outcomeshere are used for comparison purpose only with the clustering results.(PATIENT STATUS, COMBINE) = 0 “Low risk”(PATIENT STATUS, COMBINE) = 1 ”Medium risk”(PATIENT STATUS, COMBINE) = 2 ”High risk”Hence, the CM3bD model contains 48 “High risk”; 73 “Medium risk”; and 220 “Low risk” patterns.Step 3 (Data Mining Techniques): A SOM Toolbox clustering tool in the Matlab software package(SOM toolbox, 2000-2005) is used. Data is stored in a matrix of 341 x 16 (341 rows and 16 columns).A map with a size of [30, 16] is created. Note that value of 30 is length (munits); value of 16 (numberof attributes) is dim of the map; and the size of [30,16] is based on a heuristic formulas for “munits”(Alhoniemi et al, 2005) as:(5*341 (rows) ^0.54321)/4 30Figure C1: The U-matrix and each component plane for model CM3bD.The data is trained with the Best Matching Unit algorithm (BMU- SOM toolbox, 2000-2005). Thismeans all the distances between input vectors and map units (map nodes) is calculated. The greatestsimilarity (minimum Euclidean distance) to input vectors is chosen. The node here is so called winnernode. The map weight is updated; and self organizing map algorithm is continued until all the input177
vectors to be tested. The final map has quantization error of 0.438, and topographic error of 0.000. Thevisualized map of Umatrix, all components can be seen in Figure C1 below.Figure C2: The U-matrix for model CM3bD.Figure C3: The clustering results of self organizing map Kmeans algorithm.The data is distributed in U-matrix and individual map component plane (individual attribute). Forexample, “OP_DURATION” plane showed the continued data. To be clearer Figure C2 shows thedistribution of data in only U-matrix.178
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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
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 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 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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