Medical Data Mining Using Fuzzy Evolutionary Computing Abdul Razak Hussain 1 October 2002

Contents Introduction Literature Review Methodology Summary

Introduction Overview Background of problem Statement of problem Purpose of study

Overview Medical/Biological databases –broad range: dermatological, gynaecological, neurological, urological, obstetrical, immunological, etc. –vast data: 200,000 records with 60 fields, may spans decades (DW) –decision-making: operationally or strategically (DM)

Background of problem Heart disease Evolutionary computing & medical data

Heart disease 25 % of death rates in the world (developed countries). World Heart Federation: “one out of three deaths across the world are now due to heart disease and stroke”. WHO: by 2025, heart disease is the leading killer (non- communicable disease) – more common in developing countries. Kementerian Kesihatan Malaysia: heart diseases & diseases of pulmonary circulation are leading causes of deaths in govt. hospitals ( ).

Heart disease..2 Cardiovascular disease (CVD) is the main cause of death in the UK accounting for over 235,000 deaths a year: around four out of ten of all deaths. The main forms of CVD are coronary heart disease (CHD) and stroke. About half of all deaths from CVD are from CHD and about a quarter are from stroke. (source:

Heart disease..3 In 1999 CVD contributed to one-third of global deaths. Low- and middle-income countries contributed to 78 percent of CVD deaths. By 2010 CVD is estimated to be the leading cause of death in developing countries. Heart disease has no geographic, gender or socioeconomic boundaries. (Source: American Heart Association)

Evolutionary computing & medical data optimisation, automatic programming, circuit design, machine learning, economics, ecology and population genetics Solving medical problems: expert systems, ANN, evolutionary computing (EC) diabetes, breast cancer, chest pains, preterm births, cervical cancer, ovarian cancer

Statement of problem As a crucial and important procedure in medicine, diagnosis of diseases need to be performed with acceptable accuracy. Data mining technique incorporating fuzziness in evolutionary computing methods, particularly in genetic algorithm, still lacks. To develop a hybrid evolutionary algorithm to diagnose heart disease.

Purpose of study To develop a hybrid fuzzy genetic algorithm (FGA) capable of detecting heart disease based on available data sets. To compare the performance of our algorithm with other selected hybrid systems. To test the proposed algorithm in the Malaysian context.

Literature Review KDD - selection, cleaning, enrichment, coding, DM, reporting Data mining (DM) overview –Classification –Clustering –Association –Sequences

Literature Review..2 Evolutionary computing (EC): –genetic algorithm (GA) – (Holland 1975) –genetic programming (GP) – (Koza 1992) –evolution strategies (ES) – (Rechenberg 1973) –evolutionary programming (EP) – (Fogel, Owens, Walsh 1966) Soft computing (SC) = EC + ANN + FL

Methodology Research - experimental studies Research design: –development of governing equations to model various heart diseases. –selection and setting-up of relevant parameters required for the development of a hybrid fuzzy genetic algorithm (FGA). –planning of the framework in the development of the hybrid FGA. –coding of the hybrid FGA using C++/Java –testing of the algorithm using historical data. –comparison of the performance of the algorithm –testing of the algorithm using current data.

Methodology..2 Planning and execution –May 2002 until June 2005 Data sources and instrumentation Assumptions and limitations Expected results

Summary A novel hybrid fuzzy evolutionary computing algorithm that is capable of detecting heart disease, in the hope that it will achieve a better rating than other hybrid algorithms. “Evolution is the natural way to program” -Thomas Ray