Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Cleaning and Transformation tasks: These tasks are the same as above method used formodels CM1 and CM2.Attribute nameAttribute Missingtype valuesAttribute values Max Freq/MeanPATIENT_STATUS Boolean 0 Alive/Dead 713 (Alive)30D stroke/death Boolean 0 Y/N 806 (N)AGE Continuous 0 [38,93] 67.99ASA_GRADE Continuous 38 [1,4] 2.24/0.46BLOOD_LOSS Continuous 252 [0,2000] 300.45CABG_PLASTY Boolean 9 Y/N/Null 778(N)D Boolean 1 Y/N/Null 748(N)DURATION Continuous 72 [0.7-5] 1.57ECG Categorical 33Normal/Null/otherabnormal/Q-wave/ 571 (Normal)ST/A-Fib
186Otherwise, “Low risk”Hence, CM3b, and CM4b has 19 values of “Very High risk”; 107 values of “High risk”; 13 values of“Medium risk”; and 700 values of “Low risk” respectively.Attribute MissingMaxAttribute nameAttribute valuestype valuesFreq/Mean/StdevPATIENT_STATUS Boolean 0 Alive/Dead 713 (Alive)30D stroke/death Boolean 0 Y/N 806 (N)AGE Continuous 0 [38,93] 67.99ASA_GRADE Continuous 38 [1,4] 2.24/0.46D Boolean 1 Y/N/Null 748(N)HD Boolean 1 Y/N/Null 550 (N)HYPERTENSION Boolean 7 Y/N/Null 449(N)PATCH Categorical 253PTFE/Dacron/Vein/OtherVein/Stent171/341-PTFE -Dundee; 185/499 -Dacron - Hull siteRENAL_FAILURE Boolean 7 Y/N/Null 820 (N)RESPIRATORY Categorical 16Normal/Mild COAD/ModCOAD/Severe 711 (Normal)COAD/NullSEX Boolean 0 M/F 507 (M)SHUNT Boolean 14 Y/N/Null 501 (Y)St Boolean 1 Y/N/Null 565 (N)R1-A SIDE Boolean 0 Left/RightCONS Categorical 0 1;2;3;4;5 383 (4)Vascular Unit Categorical 0 1;2 498 (2)Table C17: CM4a and CM4b data structure and their summary.Step 3 (Data Mining Techniques): The same neural network techniques as above experiments areused for these models. The classification results can be seen in Table C18, and C19. The detailtopology and parameters as follows: multilayer perceptron technique is used with topologies of 16-2-1(CM3a), 14-2-1 (CM4a), 16-2-4 (CM3b), and 14-2-4 (CM4b); learning rate is 0.3; and number ofepochs is 500. Radial basis function classifier has centre parameter c of 2; and support vector machineuses poly kernel function with the exponent parameter p of 2.Step 4 (Comparison/ Evaluation): The comparisons are fulfilled based on the confusion matrix andstandard rates of sensitivity, specificity, and so on.Classifiers Risk Confusion Matrix ACC Sen Spec PPV NPV
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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
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MI(X , Y) H(X ) H(X | Y) pi,j( x,
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number of patterns in class C i ; a
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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 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 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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