Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Attribute nameAttributetypeMissingvaluesAttribute valuesMaxFreq/Mean/StdevPATIENT_STATUS Boolean 0 Alive/Dead 713 (Alive)30D stroke/death Boolean 0 Y/N 806 (N)AGE Continuous 0 [38,93] 67.99ASA_GRADE Continuous 38 [1,4] 2.24/0.46D Boolean 1 Y/N/Null 748(N)HD Boolean 1 Y/N/Null 550 (N)HYPERTENSION Boolean 7 Y/N/Null 449(N)PATCH Categorical 253PTFE/Dacron/Vein/OtherVein/Stent171/341-PTFE -Dundee; 185/499-Dacron - HullsiteRENAL_FAILURE Boolean 7 Y/N/Null 820 (N)RESPIRATORY Categorical 16Normal/MildCOAD/ModCOAD/Severe711 (Normal)COAD/NullSEX Boolean 0 M/F 507 (M)SHUNT Boolean 14 Y/N/Null 501 (Y)St Boolean 1 Y/N/Null 565 (N)R1-A SIDE Boolean 0 Left/RightCONS Categorical 0 1;2;3;4;5 383 (4)Vascular Unit Categorical 0 1;2 498 (2)6.3.6. Clinical Model 4b (CM4b)Table 6.5: CM4a and CM4b data structure and summary.The data structure for this model is the same as in the model CM4a (see Table 6.5). However, theexpected outcomes are the same as model CM3b outcomes.They are given by:IF Attr1 = "Dead" AND Attr2 = "Y" "Very High risk"Else IF Attr1 = "Dead" "High risk"Else IF Attr2 = "Y" "Medium risk"Other wise, "Low risk"85
6.4. Scoring Risk ModelsThe data for these models is selected from the Hull site and the POSSUM and PPOSSUM classifiedresults. Three scoring risk models are introduced as: Mortality, Morbidity, and Death rate. Thesemodels share the same structure (in Table 6.6) with 498 and 22 input attributes.Attribute nameAttribute MissingMaxAttribute 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 COAD/Null431(Normal)WARFARIN Boolean 2 Y/N/Null 474(N)RESP_SYSTEM Categorical 1Limiting SOB/No SOB/ Null/SOB 468 (Noat rest/ SOB in exertionSOB)BP 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; Afib 60-90;Normal; Null; Other abnormal; Q 338 (Normal)waves; ST/T Wave changeGCS(Coma Score) Continuous 1 [15] 15URGENCY Categorical 1 Elective; Scheduled urgent 497(Elective)BLOOD_LOSS Continuous 8 [100, 1800] 31886NO_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 6.6: Scoring risk models’ input structure and summary.There is a special pattern in this model with almost null values except the scoring values. This case willbe eliminated in other models. The expected outcomes for these models can be seen in Table 6.7. Theseoutcomes are calculated upon the comparison between the appropriate POSSUM and PPOSSUMoutcome values (Mortality, Morbidity, and Death rate) and their threshold values. Note that the
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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
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 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
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Page 97 and 98: Attribute nameAttributetypeMissingv
Page 99: Attribute nameAttributetypeMissingv
Page 103 and 104: are nearly the same (12). The maxim
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Page 107 and 108: epochs can help to reduce the over-
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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 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
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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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