Predicting Cardiovascular Risks using Pattern Recognition and Data ...

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Chapter 2 Risk Assessment in Medical Domains2.1. IntroductionOf all the modern technological quests, the search to create artificially intelligent computer systems hasbeen one of the most ambitious and, not surprisingly, controversial, particularly the recent applicationof artificial intelligence to decision-making areas of medicine (Coiera, 2003). Medical decision supportsystems or clinical decision support systems play an increasingly important role in medical practice(Marckmann, 2001). They are applied to broad areas of decision-making by clinicians to support theirdiagnosis based on medical data and domain knowledge. According to Coiera (2003), artificiallyintelligent computer systems, which are able to store and process vast stores of knowledge, might ablyassist clinicians with tasks such as diagnosis, and prediction the patient risk.This chapter introduces some popular risk assessment and artificially intelligent diagnostic systemssuch as MYCIN (Shortliffe, 1976); Internist/QMR (Internist/Quick Medical Reference, Miller et al,1982); the Framingham study (Framingham Heart Study, 1948); the Australian Busselton study(Knuiman et al, 1998); and the German PROCAM study (German Prospective Cardiovascular Münster,Assmann et al, 2002). These systems can be seen as a background for the thesis analysis on riskassessment systems. Some other scoring risk systems such as the Consortium for SoutheasternHypertension Control (COSEHC, 2003; Hawkins et al, 2005) and the linear scoring system (Gupta etal, 2005) are also introduced.Clinical risk assessment systems such as INdividual Data ANalysis of Antihypertensive interventiontrials (INDANA, Pocock et al, 2001), the Physiological and Operative Severity Score for theenUmeration of Mortality and Morbidity (POSSUM, Copeland et al, 1991) and the PortsmouthPOSSUM (PPOSSUM, Prytherch, 1998) are introduced and discussed in greater detail within thischapter.2.2. Risk Assessment SystemsThe intelligent medical system MYCIN (Shortliffe, 1976) is one of earliest expert systems. It wasdesigned and implemented at Stanford University in the 1970s with the purpose of diagnosing andrecommending treatment for certain blood infections. This rule-based expert system is comprised oftwo major components as follows:7
A knowledge base that stores the information of the domain expert.An inference engine to derive knowledge from the presently known knowledge in the first component(Lisboa, 2002).One of its disadvantages is the time taken to make a decision (Lisboa, 2002). Subsequently, othersystems [based on MYCIN such as EMYCIN (Melle, 1979), and PUFF (Aikins et al, 1983)] wereintroduced with improvements that speed up the decision-making time. These improvements are notdiscussed in this thesis. More detail can be seen in Lisboa (2002) and Coiera (2003).Another medical decision system is Internist/QMR (Miller et al, 1982), with a knowledge base that has956 hypotheses, which works based on diagnostic strategies such as scoring function, partitioning, orquestioning. According to Shortliffe et al (1990), the Internist/QMR mirrors hypothetic-deductivereasoning. It can handle coexistent diseases and is remarkably accurate from the start. Hence, it willmake strong assumptions about mutual independence of predictive variables. However, according toLowe (2003), it was difficult to trace the system‟s recommendations. Subsequently, its results, andassumptions, were hard to explain to clinicians.Directing attention to cardiovascular disease, the popular studies are as follows:Framingham study (Framingham Heart study, 1948);Australian Busselton study (Knuiman et al, 1998);and the German PROCAM study (Assmann et al, 2002).The Framingham study emphasises the relationships between cardiovascular disease and otherattributes such as altered blood lipid, blood pressure, body weight, and so on. The detailed review canbe seen in Gueli et al (2005). The Busselton study (Knuiman et al, 1998) used epidemiological datafrom over 8000 patients collected from 1966 to 1981. This model uses logistic regression to predict the10-year risk of coronary heart disease. Lastly, the German PROCAM study (Assmann et al, 2002) usedthe epidemiological data from 25000 patients collected from 1979 to 1985. Its logistic model predictsthe 8-year risk of a cardiovascular event for patients. Both the Busselton model and German PROCAMmodel are improved by Twardy et al (2005). They built Bayesian networks using both models as theknowledge engineering component. Cross-validation was then used to evaluate these models. Twardyet al (2005) achieved the same risk results as the use of logistic regression in the original studies.However, they pointed out that their models are easier to interpret, giving a more intuitive causal storyof coronary heart disease risk than both original models. The detail about the Busselton model, the8
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: techniques is given. The standard c
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
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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
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Page 69 and 70: where d N (X i ,Q j ) is calculated
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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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inconsistency between the linear an
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epochs can help to reduce the over-
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MSE (0.09). However, the model MLP_
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suitable for the CM3aD data set wit
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MethodA self organizing map tool is
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The SOM algorithm is applied to the
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clustering models CM3aDC and CM3bDC
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clustering results, resulting from
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and CM3bD (KMIX and SOM) suggest th
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equivalent rates for “High risk
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25 input attributes to 16 input att
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7.2.4. Scoring Risk ModelsThis sect
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increasing number of patterns impro
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other words, there are negligible o
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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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