title: Using fuzzy sets to analyze putative correlates between age, blood type, gender and/or race with bacterial infection.
abstract: Previous studies have suggested that the demographic variables of age and blood type may serve as "risk factors" for infection by specific bacterial species. Since both demographic variables and bacterial species are defined using generally accepted parameters, they constitute highly suitable variables for the generation of a fuzzy logic program. A prospective study was therefore undertaken to examine the influence of age, blood type, gender and race on bacterial infection rates using a real database generated from 187 bacteremic patients admitted to Albert Einstein Medical Center. A fuzzy logic program was created using 155 randomly selected patients' data with four input (demographic variables) and four output classes (infections with "staphylococci", "streptococci", "Escherichia coli" or "non-E. coli gram negative rods (non-E.coli GNR)"). To see whether bacterial infection could be predicted based on demographic data alone, the program was tested using the remaining 32 patients' data. The program was able to correctly determine the bacterial output group of 27 of 32 randomly selected patients, giving an overall correlation of 84.38%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a powerful diagnostic tool for all physicians.
authors.affiliation: School of Science and Health, Philadelphia University, 19144, USA. cundelld@philau.edu
authors.forename: D R
authors.initials: DR
authors.lastname: Cundell
authors.forename: R S
authors.initials: RS
authors.lastname: Silibovsky
authors.forename: R
authors.initials: R
authors.lastname: Sanders
authors.forename: L M
authors.initials: LM
authors.lastname: Sztandera
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D001424:Bacterial Infections / Q000175:diagnosis / Q000453:epidemiology; D001788:Blood Grouping and Crossmatching; D003936:Diagnosis, Computer-Assisted; D005260:Female; D005544:Forecasting; D017143:Fuzzy Logic; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D044469:Racial Groups; D018570:Risk Assessment; D012737:Sex Factors