Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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all clinical risk models (CM1-RBF, CM2-RBF, CM3a-RBF, CM3b-RBF, CM4a-RBF, and CM4b-RBF) in Chapter 7 (Sen=0 in Table 7.9). The reason for this may arise from the disadvantagesindicated in section 4.2.2 in Chapter 4. The decision tree technique (using the representative J48classifier) performs as well as the neural network classifiers through the Case Study VI in Chapter 8(see Table 8.3). However, its use is limited, because of the difficulties in finding clinically meaningfulstructural rules.Although the self organizing maps offer the benefit of a visual presentation for the data, they are notsuitable for use in this thesis as they require numerical data maps for input. This is not convenient forthe thesis data domain where a mixture of data types is given.KMIX and the improved WKMIX showed advantages in dealing with both numerical and categoricaldata via the use of Euclidean and Hamming distances. By using this clustering approach, and theresulting confirmatory classification results, the nature of the problem and the difficulty of measuringinfluential parameters are suspected for the earlier poor supervised classification results. For example,KMIX accuracy rates were at 0.60 and 0.45, and there is a big distances between the sensitivity and thepositive predictive value (see in Tables 7.5, 7.6 in Chapter 7), for the models CM3a and CM3b. Thehigh performance in the use of neural network techniques with clustering outcome models highlightedthe nature of the problem and the difficulty of measuring influential parameters (see results in Table7.7, 7.8). The improvement in the accuracy rate of WKMIX compared to the KMIX (see Table 8.2 inChapter 8) might open a new direction in the use of attribute weights in the clustering process.Generally, there is an influence of the attributes to the patient‟s outcomes in medical domain. Forexample, the INDANA trial (Pocock et al, 2001) indicated that smoking attribute is one of significantattributes in the predicting the patient risk (see section 2.2 in Chapter 2). Another example is the list ofattributes in Table 6.2 in Chapter 6 (Kuhan et al (2001), where they are estimated as the significantattributes to predict the cardiovascular risks. Therefore, attribute weights might help the clusteringprocess place data in the right classes.9.2.4. Which Method Of Clustering Data Is Appropriate For ThisMedical Domain?The thesis focuses on the use of the partitional clustering method, because of the advantageshighlighted in the literature review in Chapter 3. The K-means algorithm (Forgey, 1965; Jancey, 1966;MacQueen, 1967; Hartigan, 1975; Hartigan and Wong, 1979) is the most popular clustering tool used141
in scientific and industrial applications (Berkhin, 2002). An example of K-means, KMIX, is used withthe thesis data. The results (see in Table 4.5 in Chapter 4) show an improvement in the ability to dealwith the mixture of numerical, categorical, and Boolean attributes in the data set, by using bothEuclidean and Hamming distances to the appropriate clustering centre. The centre vector types and itsmeasures can be listed according to attribute types as follows:Numerical (continuous) attributes: The centre is the average value of all attributed values. Themeasurement method used here is Euclidean distance.Categorical (discrete) attributes: The centre is the mode (maximum frequency value) in theattribute. The measurement method is Hamming distance (see in Equation (4.17) in Chapter 4).Boolean attributes: They can be viewed as either numerical or categorical attributes. In the thesis,they are, in most cases, treated as categorical attributes.The KMIX algorithm is compared to the publicised and K-means results through the use of standarddata sets from the UCI repository (Merz & Merphy, 1996). The better results for KMIX (see in Table4.5 in Chapter 4) lead to confidence in its use for the thesis data.9.2.5. Can The Attribute Set Be Decreased By Defining The SignificantAttributes For Data Domain?The use of the filtering and ranking methods based on mutual information can reduce the number ofattributes for the data domain. The mutual information between the attributes and output classes can becalculated as Equation (8.15) in Chapter 8. This is based on Bayes‟ theorem (Bayes, 1763); the entropy(Shannon, 1948); and Kullback Leibler divergence (Cover and Thomas, 1991); given by:MI(C,x ) jpilog(q . rc sjk pijki1 k1i jk),wherepijksumiqi ,sumsumijksumwith sum ijk is number of patterns in class C i, with attribute j th and in the k th state,sumwhere sum i is number of patterns belong to class C i , and r jkjksum,where sum jk is number of patterns in attribute j th and in k th state.142
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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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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 152: and the prediction risks reflects i
Page 153 and 154: Adding attribute weights to the clu
Page 155: system. Moreover, in POSSUM and PPO
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 204 and 205: 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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