Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Importantly, the WKMIX gap between sensitivity and positive predictive value is a little smallercompared to the KMIX (about 0.30 and 0.48 respectively).Like the framework of the Case Study IV for discovering the data structure, the clustering modelCM3aDC is generated with the outcomes derived from the WKMIX results. This new model is thenused with the same neural network techniques as in the Case Study IV. For example, the multilayerperceptron with a topology of 16-0-1 (16 input nodes; 0 hidden node; and 1 output - 2 class nodes) isused; its learning rate is 0.3; 100 training cycles; and ten-fold cross-validation (see detail in Table 8.3).The use of the J48 decision tree technique is also applied to this new model.Classifiers Risk C2H C1L ACC Sen Spec PPV NPV MSECM3aDC-MLP(16-0-1; 0.3;100epochs)CM3aDC-RBF(c=1)CM3aDC-SVM(poly; p=1)CM3aDC-J48(binary tree)C2H 135 0C1L 0 206C2H 132 3C1L 6 200C2H 135 0C1L 0 206C2H 135 0C1L 0 206Relief algorithm. However, it is simpler to use than the Relief algorithm.1371 1 1 1 1 00.97 0.97 0.98 0.99 0.96 0.021 1 1 1 1 01 1 1 1 1 0Table 8.3: The results of CM3aDC used alternative techniques.Table 8.3 shows that all classifiers achieve nearly ideal results over all standard rates. For example, atthe confusion matrix of the poorest classifier CM3aDC-RBF, there are only 3 mis-classified patterns(“C2H”) in the “C1L” class whereas there are 6 mis-classified “C1L” in the “C2H” class. Importantly,there is no distance between the sensitivity versus positive predictive value, and the specificity versusnegative predictive value except the very negligible gap (0.02) of the classifier CM3aDC-RBF. Again,these results demonstrate that pattern recognition techniques (neural networks and decision tree of J48)can replicate the clustering results derived from the WKMIX outcomes. Therefore, as discussed inChapter 7, the nature of the problem and the difficulty of measuring influential parameters againinfluence the clustering WKMIX results in Table 8.2.8.6. DiscussionFigure 8.2 and Figure 8.3 show that the mutual information performance is very similar to the popular
Adding attribute weights to the clustering algorithm does not change the computational cost of thealgorithm compared to the KMIX. The WKMIX results (Table 8.2) clearly show an improvement inaccuracy compared to KMIX. Furthermore, the high performances (ideal rates) in Table 8.3 show thesupervised techniques can perfectly replicate the outcomes of the WKMIX clustering models. Thismeans the data is formed into well defined clusters by the WKMIX algorithm. This suggests that byadding weights in the clustering process, the quality of the algorithm is improved.8.7. SummaryThis chapter presented the concept of feature selection. A simple (filter) feature selection method isapplied to the thesis data to measure the relevant level of attribute set to the outcomes. The improvedWKMIX algorithm provides an investigation of using attribute weights (mutual informationcalculations) in the clustering process. The Case Study VI results proved this by showing the increasingperformance compared to the KMIX.138
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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
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_
Page 111 and 112: suitable for the CM3aD data set wit
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
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 the prediction risks reflects i
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 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:
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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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