Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Scoring values: The original data included the physiological score and operative severity score valuestaken from the POSSUM and PPOSSUM systems. Furthermore, the data in this site includes enoughinformation for use separately in the POSSUM and PPOSSUM calculations.The valid or missing value frequencies of some significant attributes can be seen in Table 5.1. Theseattributes are labelled as "PATIENT_STATUS"; "Heart Disease”; "Diabetes”; and “Stroke”. Theseattributes are highlighted in the research of collaborative clinicians (Kuhan et al., 2001), as some of themain factors expected to contribute to the outcomes for patient risks.NumberValidMissingDiabetes Heart Disease Stroke PATIENT_STATUS497 497 497 4981 1 1 0Table 5.1: The frequencies of significant attributes in the Hull site data.It is clear from Table 5.1 that there is one case (1 out of 498) that includes missing values in all thesignificant attributes except the “PATIENT_STATUS” attribute. However, the “PATIENT_STATUS”attribute is the most significant, and this attribute will be the main factor for outcome calculations inlater chapters. Therefore, this case with some missing values will not be eliminated. Its missing valueswill be filled by the use of data mining method (see detail in “Data Selection Strategy” section).Dundee Site DataThis data includes 57 attributes, and 341 cases from cardiovascular patients at the Dundee site. Thedetailed structure of the original data can be seen in Appendix A. This data site has similarcharacteristics to the Hull site such as redundant attributes, missing values, and noisy values. Themethod of data treatments such as elimination of redundant attributes, filling the missing data, and soon is based on the strategy indicated in “Data Selection Strategy” section below. The maincharacteristics can be seen as follows:Redundant attributes: For example, the attribute “ADMISSION_DATE” shows patient‟s operationdate; or the two attributes “Surgeon name1” and “Surgeon name2” represents names of operatingdoctors. Their values might be helpful in a general evaluation, but offer little relevance to the specificpurposes of this thesis.67
Missing values: The data includes 1912 output of 12311 cells with missing values (16%) after deletionof the above redundant attributes.Noisy and inconsistent data: As an example of numerical outlier values, the attribute "PACK YRS"has a big gap between the maximum value of 160, and the minimum value of 2. This affects thetransformation process as it unduly changes the mean of the attribute values.Scoring values: The site does not include the scored values (physiological score, and operative severityscore) from the POSSUM and PPOSSUM systems. Furthermore, the data in this site is insufficient touse with the scoring systems of POSSUM and PPOSSUM, as it lacks information for these systems‟variables.To complete a similar analysis, as with the Hull site, the valid or missing value frequencies for somesignificant attributes can be seen in Tables 5.2 below.Heart30 D stroke/deathDiabetes StrokeDiseaseN Valid 341 334 341 340Missing 0 7 0 1Table 5.2: The frequencies of the significant attributes for the Dundee site.Table 5.2 shows that the attribute “Heart Disease” has 7 missing values whereas there is only onemissing value for the “Stroke” attribute. The method of dealing with missing values is identical to themethod indicated for the Hull site.5.3.2. Thesis Experimental StepsThe detailed steps of the thesis methodology can be redrawn as shown in Figure 5.4. The process flowand individual steps in Figure 5.4 can be illustrated in detail as follows:Step 1 (Selection): A data set is selected from one or both of the Hull and Dundee sites, and stored inthe “Data Warehouse”. Note that data from both sites were collected and stored in various (Excel)computer files in earlier studies. The data here is understood as “Raw data” in the “knowledgediscovery from data” process. Therefore, the other process steps such as pre-processing andtransformation steps are needed.68
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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
Page 31 and 32: R11e3 z2Reg.No PATIENT_STATUS Physi
Page 33 and 34: designed to their specific purposes
Page 35 and 36: Chapter 3 Pattern Recognition3.1. I
Page 37 and 38: algorithms depending on the request
Page 39 and 40: a new patient. For example, t 5 can
Page 41 and 42: And the strategy for pattern learni
Page 43 and 44: Hence, whenever there is a specific
Page 45 and 46: 3.4.2. Nonlinear ModelsDefinition:
Page 47 and 48: vector machine are nonlinear models
Page 49 and 50: From the confusion matrix in Table
Page 51 and 52: the accuracy of 0.90 shows the trad
Page 53 and 54: The comparison between neural netwo
Page 55 and 56: Chapter 4 Supervised and Unsupervis
Page 57 and 58: Therefore, the visualization of pat
Page 59 and 60: A popular form of Gaussian basic fu
Page 61 and 62: mj 1w ( x) b 0jj(4.7)where x is a
Page 63 and 64: as an instance of the popular K-mea
Page 65 and 66: o Update the weight as follows:w i
Page 67 and 68: Discrete (categorical) attributes:
Page 69 and 70: where d N (X i ,Q j ) is calculated
Page 71 and 72: Zoo SmallThis data contains 101 cas
Page 73 and 74: 1.20Discussions1.000.800.600.400.20
Page 75 and 76: Chapter 5 Data Mining Methodology a
Page 77 and 78: Raw dataDataselectingTarget DataPre
Page 79 and 80: missing data”, the more detailed
Page 81: Step 5 (Comparison/ Evaluation): Th
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_
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
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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
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Adding attribute weights to the clu
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system. Moreover, in POSSUM and PPO
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in scientific and industrial applic
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eing located in alternative risk ba
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and computer scientists, to verify
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Baxt, W. G. (1992). Use of an Artif
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Cristianini, N., Shawe-Taylor, J. (
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Hawkins, R.G., Houston, M.C., Ferra
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Kohonen, T. (1981). Self-organized
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Manning, C.D., Raghvan, P., and Sch
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Ohn, M. S., Van-Nam, H., and Yoshit
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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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