A Fuzzy Approach for Diabetes Mellitus Type 2 Classification

Bressan, Gláucia Maria; Azevedo, Beatriz Cristina Flamia de; Souza, Roberto Martins de

doi:10.1590/1678-4324-2020180742

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…To analyze the given problem, we used the adaptive neuro-fuzzy interference system (ANFIS) network type, which is supervised learning with fuzzy logic that is similar to Takagi and Sugeno's approach. The process of learning a neural network with phase logic, Figure 1, represents a complex structural learning of linking input parameters that do not have clearly de ned boundaries and their impact with a certain degree of state severity in linking to target values as output parameters [25,26].…”

Section: Neuro-fuzzy Methodsmentioning

confidence: 99%

Monitoring of Kidney Functions, Electrolytes and Volemia Analyzed by Application Example of Artificial Intelligence in Patients with Heart Failure

Tasić

Djordjević

Galović

et al. 2021

Preprint

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Basal renal function is a predictor of response to diuretic therapy and marker of poor prognosis. Simultaneous changes in renal function, sodium, potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. In our paper, an analysis of volemia, electrolytes, and renal function in heart failure was performed using an algorithm based on the ANFIS (Adoptive Neural Fuzzy Inference System), an intelligent approach to renal and heart function monitoring. The study included 90 subjects who were divided into two groups: clinical (n-80) and control (n-10). The base is composed of parameters B-type natriuretic peptide (NT-proBNP), sodium (Na), potassium (K), ejection fraction (EF), EPI creatinine-cystatin C formula and ANFIS expert system combined in neural network and fuzzy logic network. The results showed that the overall trend of data verification in the network with NT-proBNP, Na and K that we formed is approximately 15%, with which subjects can be classified according to the severity of hypervolemia, electrolyte disturbance and renal function. NT-proBNP (pg/mL) had the most influence on the EPI creatinine-cystatin C formula. Serum sodium (Na) has the most influence on the ejection fraction (EF).

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Section: Neuro-fuzzy Methodsmentioning

confidence: 99%

Monitoring of Kidney Functions, Electrolytes and Volemia Analyzed by Application Example of Artificial Intelligence in Patients with Heart Failure

Tasić

Djordjević

Galović

et al. 2021

Preprint

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show abstract

“…En el 2020, Bressan, Flamia de Azevedo y Molina de Souza [6], desarrollan un clasificador difuso para catalogar a un grupo de personas como pacientes que requieren atenciones básicas o que requieren atenciones especializadas. Partiendo de los datos clínicos de edad, triglicéridos, tiempo de evolución de la enfermedad, IMC, ingresos per cápita, circunferencia abdominal y tiempo de escolaridad proporcionados por el Unified Health System de Brasil, un experto descartó los atributos no esenciales y, usando un árbol de decisión con el algoritmo C4.5 se generaron todas las reglas requeridas por el sistema.…”

Section: Estado Del Arteunclassified

Determinación del riesgo de diabetes en México mediante un sistema difuso optimizado por recocido simulado

Márquez

Ruiz

Juárez

et al. 2022

RITI

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According to the World Health Organization, approximately 70% of adults in Mexico are overweight or obese, determining factors in the development of diabetes mellitus type 2. In addition, according to the National Institute of Public Health, 10.3% of those over 20 years old suffer from diabetes. To facilitate decision or classification tasks when treating a patient, experts develop systems based on fuzzy logic, however, this design is not usually infallible, so it is common to optimize them to improve their performance. The present work shows the results of a comparison between the efficiency in predicting the risk of suffering from type 2 diabetes established by the FINDRISC test and an own design fuzzy system optimized by the Simulated Annealing heuristic for 295 patients from Acapulco, Mexico. The comparison shows that the fuzzy system obtains the same sensitivity, but higher specificity values and positive and negative predictive values with general improvement in the confidence intervals, concluding that using the proposed system as an aid in the prevention of type 2 diabetes is viable and yields results attached to the reality of the patients.

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“…Ahmadi, H. proposed a method for diseases diagnosis by using fuzzy logic methods, which is a systematic and meta-analysis review [10]. Bressan, G. M. also suggested a system for diabetes mellitus type-2 classifications which was based on a fuzzy approach [11]. But, due to the consideration of membership grade only, fuzzy logic failed to handle the uncertainty, presents in the diagnosis process.…”

Section: Introductionmentioning

confidence: 99%