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Table C19: Experimental results of CM3b and CM4b models.C.5.3. Scoring Risk modelsStep 1 (Selection): The data is selected from the Hull site and the POSSUM and PPOSSUM results.The models are built as Mortality, Morbidity, and Death rate. Note that these models share the samestructure (see in Table C20) including 499 cases and 22 input attributes.Step 2 (Clean/Transform/Filter):Cleaning task: The method of filling missing values is the same as experiments above. Thismeans, for example, continuous missing values are replaced by the mean of non-missingnumerical values. Note that the second column in Table C20 shows number of missing valueswhereas the last column shows replacing values if applicable. For example, the missing valuesin the attribute WCC are replaced by the mean (7.67).Transformation task: This task is to transform all data to numerical data type. This means thenumerical data is rescaled in to the range of [0,1]. Boolean values are transformed into values of0 or 1. The categorical data is transformed into discrete Boolean (“Normal” and “Abnormal”)then they are transformed into values of 0 or 1.Filtering task: Some attributes in these models structure can be eliminated. They are JVP, GCS(Coma Score), URGENCY, OP-SEVERITY, MALIGNANCY, and PERI_SOILING (see thesummary in Table C20). For example, the attribute namely JVP contained only value of “N”, orMALIGNANCY contained only value of “None” as well. Hence, the data sets contain now 16input attributes and 497 cases.The outcome for three models are calculated basing on the average (mean) values of Mortality,Morbidity, and Death rate scores as follows:.Mortality model:IF Mortality>= meanOtherwise “Low risk” “High risk”Morbidity model:IF Morbidity>= mean “High risk”189
Otherwise “Low risk”Death rate model:IF Death rate>= mean “High risk”Otherwise “Low risk”Attribute MissingMaxAttribute nameAttribute valuestype valuesFreq/MeanPhysiolScore Continuous 0 [12,41] 20.37OpSevScore Continuous 0 [13,23] 14.29AGE Continuous 0 [38,93] 67.99RESPIRATORY Categorical 1Normal/MildCOAD/ModCOAD/ Severe 431(Normal)COAD/NullWARFARIN Boolean 2 Y/N/Null 474(N)RESP_SYSTEM Categorical 1Limiting SOB/No SOB/Null/SOB at rest/ SOB 468 (No SOB)in exertionBP Continuous 21 [90,220] 151.9PULSE Continuous 23 [42,110] 74JVP Boolean 2 N 495(N)WCC Continuous 10 [4, 24.3] 7.67HAEMOGLOBIN(Hb) Continuous 10 [7.7,18.2] 13.9UREA Continuous 8 [2.1, 17.2] 6.34SODIUM(Na) Continuous 11 [122, 146] 138.5POTASSIUM(Ka) Continuous 9 [3, 5.6] 4.3ECG Categorical 16≥5 ectopics/min; Afib60-90; Normal; Null;Other abnormal; Q 338 (Normal)waves; ST/T WavechangeGCS(Coma Score) Continuous 1 [15] 15URGENCY Categorical 1Elective; Scheduledurgent497(Elective)BLOOD_LOSS Continuous 8 [100, 1800] 318NO_PROCSDiscretenumber59 [1; 2; 3] 418 (1)OP_SEVERITY Categorical 0 Major Plus497 (MajorPlus)MALIGNANCY Categorical 0 None 497 (None)PERI_SOILING Categorical 0 None 497 (None)Table C20: Scoring Risk models’ input structure and their summary.190
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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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The results in Figure 8.3 show that
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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 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 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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