Predicting Cardiovascular Risks using Pattern Recognition and Data ...

More documents

Recommendations

Info

Appendix A. <strong>Data</strong> structureA.1. Hull siteThe following table summarises the data from the Hull clinical site. As can be seen there are 30numeric, 3 discrete numeric, 23 categorical, 38 Boolean, <strong>and</strong> 4 date/time typed attributes.AttributeAttributeAttribute nametypes Attribute nametypesUNIT_NO Categorical JVP BooleanTHEATRE_SESSION_DATE Date/Time LEG_OEDEMA BooleanCONS Discrete PULM_OEDEMA BooleanDATE_OF_DEATH Date/Time CARDIAC_FAIL BooleanCombined Categorical HAEMOGLOBIN Numeric30D MR Boolean WCC Numeric30D Ipsi CVA Boolean PLATELETS NumericCAUSE_OF_DEATH Categorical UREA NumericPhysiolScore Numeric CREATININE NumericOpSevScore Numeric SODIUM NumericP-POSS(2) Numeric POTASSIUM NumericP-POSS(1) Numeric GLUCOSE NumericPOSS Numeric INR NumericD Boolean PAO2 NumericHD Boolean ECG CategoricalSt Boolean CXR CategoricalCODE Categorical PULM_CXR CategoricalCAROTID_DISEASE Categorical URGENCY CategoricalARRHYTHMIA Boolean DURATION NumericANGINA Boolean CONSULTANT_PRESNT BooleanMYOCARDIAL_INFARCT Categorical ASA_GRADE DiscreteCCF Boolean ANAESTHETIC_TYPE BooleanDIABETES Categorical CRYSTALOID_VOL NumericSEX Boolean COLLOIDS NumericPATIENT_STATUS Boolean TRANSFUSION NumericINDICATION Categorical OTHER_BLOOD NumericPVD Categorical BLOOD_LOSS NumericDATE_HISTORY Date/Time LOWEST_BP NumericAGE Numeric MIN_TEMP NumericHYPERTENSION Boolean INOTROPES BooleanRENAL_FAILURE Boolean PRIMARY_OP Boolean165
HYPERCHOLESTEROLAEMIA Boolean OPERATION_DESC CategoricalALLERGIES Boolean NO_PROCS DiscreteSMOKING Categorical OP_SEVERITY CategoricalPACK_YEARS Numeric PERI_SOILING BooleanRESPIRATORY Boolean MALIGNANCY BooleanAMBUL_STATUS Categorical LETTER_TEXT CategoricalCABG_PLASTY Boolean PROCEDURE_RANK NumericTHROMBO_EMBOLISM Boolean SHUNT BooleanEJECT Numeric PATCH CategoricalDIURETICS Boolean COMP_GROUP CategoricalWARFARIN Boolean COMPLICATION CategoricalDIGOXIN Boolean SEVERITY CategoricalANTIHYPERTENSIVES Boolean COMPLICATION_DATE Date/TimeSTEROIDS Boolean RESP_SYSTEM CategoricalANTI_ANGINAL Boolean GCS NumericSTATINS Boolean BUILD BooleanASPIRIN Boolean BP NumericORTHOPNOEA Boolean PULSE Numeric166
Page 1 and 2:
THE UNIVERSITY OF HULLPredicting Ca
Page 3 and 4:
3.4.2. Nonlinear Models ...........
Page 5 and 6:
8.3. Mutual Information ...........
Page 7 and 8:
List of FiguresFig. 3.1 Typical pat
Page 9 and 10:
List of TablesTable 2.1The 11 facto
Page 11 and 12:
Table 6.14 Alternative number of cr
Page 13 and 14:
Table C12 Neural network results of
Page 15 and 16:
AbbreviationsNN(s): Neural Network(
Page 17 and 18:
Chapter 1 IntroductionThis thesis p
Page 19 and 20:
on. According to Bishop (1995), the
Page 21 and 22:
techniques is given. The standard c
Page 23 and 24:
A knowledge base that stores the in
Page 25 and 26:
This score is then compared to the
Page 27 and 28:
Cardiac signsRespiratory signsSysto
Page 29 and 30:
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 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_
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 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 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
Page 206 and 207: Therefore, the Mortality has got 25
Page 208 and 209: Both models CM3aC and CM3bC are use
Page 210 and 211: R1 PATECGAMBUL_STATUR1-A SIDEANTI_A
Page 212: Step 5 (Building New Models): The n
show all

Predicting Cardiovascular Risks using Pattern Recognition and Data ...

Create successful ePaper yourself

Delete template?

Save as template?