A fuzzy logic model for differential diagnosis of lower urinary tract dysfunctions

Abstract

Lower urinary tract dysfunctions (LUTD) restrict quality of life, resulting in decreased work productivity and emotional well-being. However, most people are not diagnosed because they do not seek medical treatment. In addition, some facilities do not adequately train health professionals in the evaluation, diagnosis and treatment of these conditions. The study's objective was to develop a decision support system modelled on fuzzy logic that defines LUTD using the terminology of the International Continence Society. This methodological study aimed to develop a model that uses the maximum-minimum composition (max-min) of fuzzy relations that can perform differential diagnoses of LUTD. The model was tested in 100 cases (50 men and 50 women), and the data were obtained from medical records containing the clinical data and results of urodynamic studies. All medical records were reviewed by a specialist in urology. The model was capable of determining a diagnosis in full (62%) or partial (36%) agreement with the medical report. Agreement between the model and the medical report was excellent (kappa = 0·98, p ≪ 0·001, CI = 0·88-1) or substantial (kappa = 0·53, p ≪ 0·001, CI = 0·45-0·60), considering overestimative accordance (where accordance is assumed when at least one diagnosis is equal) and underestimative accordance (where accordance is assumed when all diagnoses are equal), respectively. The proposed model based on the max-min composition of fuzzy relationships is very simple and performed well. However, more tests are recommended before the model is used as a decision support system. RATIONALELower urinary tract dysfunctions (LUTD) and symptoms (LUTS), such as urinary incontinence (UI), nocturia and urgency, restrict the quality of life for men and women with these conditions (Robertson et al., 2007), leading to a decrease in work productivity and emotional well-being (Coyne et al., 2010). LUTS also affect spouses of men with this health problem, causing mental, emotional and physical suffering (Kim and Lee, 2009).Symptoms are common among women. In a study of 907 female nurses, aged 26-35, most had an incidence of 65% (590) of at least one case of LUTS (Liao et al., 2009). Another study conducted in India involving female housewives (75), workers (75) and adolescents (180) found an overall prevalence of 18·5% (61) (Avasarala et al., 2008). However, a study conducted in Sweden showed that the prevalence of UI and overactive bladder (OAB) had not changed over 16 years. In 1991 and 2007, 2911 and 3158 women, respectively, participated in the study; only women aged 65-79 reported more UI in 2007 than in 1991

Behavior patterns in hormonal treatments using fuzzy logic models

“…Also, fuzzy logic provides a systematic calculation for processing knowledge that is uncertain, imprecise or with incomplete information (Bui et al 2012). Nowadays, there are some fuzzy logic models studies which provide different solutions in many medical areas: Lopes et al (2015) describe a decision support system modeled on fuzzy logic that defines lower urinary tract dysfunctions (LUTD) using the terminology of the International Continence Society. This methodological study aimed to develop a model that uses the maxmin composition of fuzzy relations that can perform differential diagnoses of LUTD.…”