AtherEx: an Expert System for

Atherosclerosis Risk Assessment

Petr Berka, Vladimír LašUniversity of Economics, Prague

Marie Tomečková Institute of Computer Science,

Prague berka@vse.cz

AIME 2005 2

Atherosclerosis

slow buildup of deposits of fatty substances, cholesterol, body cellular waste products, calcium, and fibrin (a clotting material in the blood) in the inside lining of an artery. The buildup (refered as a plaque) with the formation of the blood clot (thrombus) on the surface of the plaque can partially or totally block the flow of blood through the artery. If either of these events occurs and blocks the entire artery, a heart attack or stroke or other lifethreatening events may result.

AIME 2005 3

Risk Factors of Atherosclerosis

non-affectable: sex, age, family history

affectable: blood pressure, level of cholesterol, smoking,

factors of life style nourishment (obesity) physical activities reaction on stress

many other factors

AIME 2005 4

Cardiovascular Disease (CVD) Risk Calculators

system questions suitable for results

NCEP ATP III 11 + 2 all patients CVD risk in 10 years

Risk assessment tool

4 + 2 all patients IM risk in 10 years

Framingham Risk Assessment

5 + 2 all patients IM risk in 10 years

PROCAM Risk Calculator

6 + 3 middle-aged men IM risk in 10 years

PROCAM Risk Score

7 + 4 middle-aged men IM risk or death on CVD in 10 years

PROCAM Neural Net

11 + 5 middle-aged men IM risk in 10 years

Heart Score 4 + 2 middle-aged patients death on CVD in 10 years

AIME 2005 5

CVD Calculators Expert Systems

Calculators evaluate risk as weighted sum of all

factors user must give exact answers to all

questions Expert Systems

evaluate risk by inference in a rule base can handle uncertain or missing

information

AIME 2005 6

Expert System NEST (1/2) Knowledge representation

attributes (binary, nominal, numeric) and propositions

rules: condition conclusion (weight), action compositional - each literal in conclusion has a weight apriori - compositional rules without condition logical - non-compositional rules without weights

Inference as a combination of backward and forward chaining compositional inference for compositional and

apriori rules (combining contributions of rules) non-compositional inference for logical rules

(modus ponens + disjunction)

AIME 2005 7

Expert System NEST (2/2) Uncertainty processing

uncertainty possible in both expert’s knowledge and in user’s answers during consultation,

compositional approach (combining contributions of all applicable rules) based on algebraic theory of P. Hájek

different sets of combination functions (MYCIN + PROSPECTOR like, Lukasiewicz many-valued logic, neural networks like)

two basic modes of consultation: dialogue and questionnaire,

implemented as stand-alone or client-server version. http://lisp.vse.cz/NEST

AIME 2005 8

Basics of the AtherEx System

Knowledge Base created in two-step process machine learning algorithm applied to data from

an epidemiological study of atherosclerosis prevention

obtained rules revised and refined by an expert system works mainly with risk factors easily

understandable by non expert users (20 factors + 1 lab. test)

result of consultation is the classification of a patient into one of four groups w.r.t atherosclerosis risk.

http://j116h1.vse.cz

AIME 2005 9

Atherosclerosis risk factors study

Longitudinal (1975-2000) study of atherosclerosis risk factors in the population of middle-aged men divided into three groups (normal, risk, pathological).

to identify atherosclerosis risk factors prevalence in a population of middle-aged men,

to follow the development of these risk factors and their impact on the examined men health, especially with respect to atherosclerotic CVD,

to study the impact of complex risk factors intervention on development of risk factors and CVD mortality,

to compare (after 10-12 years) risk factors profile and health of the selected men in different groups.

AIME 2005 10

Data STULONG

Entry

1417x64

Control

10572x66

Letter

403x62

Death

389x5

AIME 2005 11

Rule induction algorithm KEX

Decision rules in the form Ant Class (w)

Compositional algorithm Building rules by a knowledge refinement

process (add new - more specific - rule only if it will improve the classification)

Applying rules by combining contributions of all relevant rules using a pseudo-bayesian formula:

)1()1( 2121

2121 wwww

wwww

http://lispminer.vse.cz

AIME 2005 12

STULONG ENTRY table analyses (1/2)

classification based only on already known risk factors,

classification based on attributes concerning life style, personal and family history (but without special laboratory tests),

classification based on attributes concerning life style and family history,

classification based only on attributes concerning life style.

AIME 2005 13

STULONG ENTRY table analyses (2/2)

AIME 2005 14

Modifications suggested by Domain Expert and Expert

users use the goals "no risk", "low risk",

"medium risk" and "high risk" instead of original groups taken from data,

add rules for remaining values of an attribute, if at least one value of this attribute occur in rules obtained from data,

add the attribute "total cholesterol" and the respective rules,

split some questions.

AIME 2005 15

Implementation client-server version of NEST used

(client is a web browser) front-end hides details about inference

and uncertainty processing (the developer can design the layout of dialogue for each knowledge base)

dialogue mode of consultation (with the possibility to change answers after consultation using questionnaire)

AIME 2005 16

Screenshot of AtherEx (1/2)

AIME 2005 17

Screenshot of AtherEx (2/2)

AIME 2005 18

Conclusions and future work

We developed a system that should help non-expert users to determine their atherosclerosis risk

The system can infer a conclusion from incomplete and/or uncertain input information

Our experiments have shown that the information about life style can substitute laboratory tests

We plan to include knowledge dealing with the dynamics of the risk factors