AtherEx: an Expert System for Atherosclerosis Risk Assessment Petr Berka, Vladimír Laš University of Economics, Prague Marie Tomečková Institute of Computer Science, Prague

AIME 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 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 Cardiovascular Disease (CVD) Risk Calculators systemquestionssuitable forresults NCEP ATP III11 + 2all patientsCVD risk in 10 years Risk assessment tool 4 + 2all patientsIM risk in 10 years Framingham Risk Assessment 5 + 2all patientsIM risk in 10 years PROCAM Risk Calculator 6 + 3middle-aged menIM risk in 10 years PROCAM Risk Score 7 + 4middle-aged menIM risk or death on CVD in 10 years PROCAM Neural Net middle-aged menIM risk in 10 years Heart Score4 + 2middle-aged patientsdeath on CVD in 10 years

AIME 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 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 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.

AIME 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.

AIME Atherosclerosis risk factors study Longitudinal ( ) 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 years) risk factors profile and health of the selected men in different groups.

AIME Data STULONG Entry 1417x64 Control 10572x66 Letter 403x62 Death 389x5

AIME 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:

AIME 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 STULONG ENTRY table analyses (2/2)

AIME 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 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 Screenshot of AtherEx (1/2)

AIME Screenshot of AtherEx (2/2)

AIME 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