Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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ClassifiersCM3b-MLPCM3b-RBFCM3b-SVMCM4b-MLPCM4b-RBFCM4b-SVMRiskVery HighriskConfusion MatrixVeryHighriskHighriskMediumriskLowrisk2 1 0 16High risk 0 3 0 104Mediumrisk0 0 0 13Low risk 5 13 0 682Very Highrisk0 0 0 19High risk 0 0 0 107Mediumrisk0 0 0 13Low risk 0 0 0 700Very Highrisk0 3 0 16High risk 1 6 0 100Mediumrisk0 0 1 12Low risk 13 46 8 633Very Highrisk0 1 0 18High risk 0 8 0 99Mediumrisk0 1 0 12Low risk 0 27 0 673Very Highrisk0 0 0 19High risk 0 0 0 107Mediumrisk0 0 0 13Low risk 0 0 0 700Very Highrisk2 4 0 13High risk 2 11 0 94Mediumrisk0 0 0 13Low risk 17 45 9 629ACC SenTable 7.3: Experimental results of CM3b and CM4b models.Spec PPV NPV0.84 0.04 0.97 0.25 0.840.85 0.00 1.00 N/A 0.830.79 0.08 0.90 0.14 0.830.83 0.07 0.96 0.27 0.840.85 0.00 1.00 N/A 0.830.8 0.14 0.90 0.21 0.84111
7.2.4. Scoring Risk ModelsThis section demonstrates the results of the POSSUM and PPOSSUM systems via the scoring riskmodels as Mortality, Morbidity, and Death rate. Note that in the confusion matrix, the expectedoutcomes (“High risk” and “Low risk”) are derived from the actual number of “Dead” and “Alive”patients. Table 7.4 shows the POSSUM and PPOSSUM results via the use of standard measurements.Overall, all classifiers (Mortality, Morbidity, and Death rate) produce the similar standard measurementrates. For example, all accuracy rates are at about 0.83. From this point, the results seem to show thatPOSSUM and PPOSSUM classifiers have a stable performance in their classification process for thethesis data.Confusion MatrixClassifiersRiskHigh riskLow riskACC Sen Spec PPV NPVMortalityMorbidityDeath rateHigh risk 10 69Low risk 15 405High risk 15 64Low risk 24 396High risk 10 69Low risk 16 4040.83 0.13 0.96 0.40 0.850.82 0.19 0.94 0.38 0.860.83 0.13 0.96 0.38 0.85Table 7.4: Confusion matrix for scoring risk models.Nevertheless, there still is a big gap between numbers of the correct “High risk” and the correctpredictive “High risk” via the sensitivity and the positive predictive value. For example, the Mortalityclassifier predicts 13% of correct predicted “High risk” (sensitivity of 0.13) whereas it predicts 40% ofcorrect predictive “High risk” (positive predictive value of 0.40). Also according to this classifier‟sconfusion matrix, 10 over 79 (13%) “Death” patients (“High risk”) are correctly predicted whereas 69over 79 “Death” patients are mis-predicted into “Alive” (“Low risk”) class. This might mean that these(69) mis-classified patients might have pattern forms (original data) similar to the “Alive” patients. Incontrast, the vertical comparison in the confusion matrix shows 15 “Alive” (“Low risk”) patients aremis-predicted into “Dead” (“High risk”) class, perhaps because their pattern forms are similar to “High112
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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
Page 75 and 76: Chapter 5 Data Mining Methodology a
Page 77 and 78: Raw dataDataselectingTarget DataPre
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Page 81 and 82: Step 5 (Comparison/ Evaluation): Th
Page 83 and 84: Missing values: The data includes 1
Page 85 and 86: Scoring Risk Models: These are buil
Page 87 and 88: The missing values for the “Heart
Page 89 and 90: Data Filtering StageIn this step, t
Page 91 and 92: Supervised ClassifiersThis section
Page 93 and 94: 5.4. SummaryData mining is a partic
Page 95 and 96: the significant level of 98.6% (P v
Page 97 and 98: Attribute nameAttributetypeMissingv
Page 99 and 100: Attribute nameAttributetypeMissingv
Page 101 and 102: 6.4. Scoring Risk ModelsThe data fo
Page 103 and 104: are nearly the same (12). The maxim
Page 105 and 106: inconsistency between the linear an
Page 107 and 108: epochs can help to reduce the over-
Page 109 and 110: MSE (0.09). However, the model MLP_
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Page 113 and 114: 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
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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
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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
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Page 153 and 154: Adding attribute weights to the clu
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Page 162 and 163: and computer scientists, to verify
Page 164 and 165: Baxt, W. G. (1992). Use of an Artif
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Page 168 and 169: Hawkins, R.G., Houston, M.C., Ferra
Page 170 and 171: Kohonen, T. (1981). Self-organized
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Simelius, K., Stenroos, M., Reinhar
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Wang, K. Wang, L. Wang, D. and Xu,
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Appendix A. Data structureA.1. Hull
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A.2. Dundee siteThe following table
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B.2. Scoring Risk ModelsTable B2 sh
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The results are then divided to dif
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Attribute NameOriginal data Transfo
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AttributeName175Attribute TypeMissi
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Filtering task: The expected outcom
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Data is clustered by SOM Kmeans alg
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197 values of “C2H”. The model
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PATIENT_STATUS Boolean 0 Alive/Dead
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Cleaning and Transformation tasks:
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High riskLow riskCM3a-MLPCM3a-RBFCM
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Table C19: Experimental results of
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Therefore, the Mortality has got 25
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Both models CM3aC and CM3bC are use
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R1 PATECGAMBUL_STATUR1-A SIDEANTI_A
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Step 5 (Building New Models): The n
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