A Fuzzy Approach for Diabetes Mellitus Type 2 Classification

Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an "expert" fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional.

Respiratory prediction algorithm based on adaptive neuro-fuzzy inference system / 国际生物医学工程杂志

Objective To study an accurate prediction algorithm for irregular respiratory movements,effectively compensate for the time delay of radiotherapy systems,so as to improve the target accuracy of imageguided tracking or gated radiotherapy for thoracic and abdominal tumors.Methods A prediction algorithm based on an adaptive neuro-fuzzy inference system (ANFIS) was proposed to precisely predict irregular respiratory motion.The ANFIS model structure adopted the position and velocity of respiratory motion as input parameters to construct an N×N fuzzy set that combines position and velocity,and to establish a training set through historical data.In the prediction,if the position or velocity in the latest input signal was out of the range of the training set,the position or velocity would be adjusted accordingly and treated as the input parameter of the ANFIS model for prediction.The ANFIS was evaluated using 20 cases of irregular respiratory clinical data from CyberKnife-treated thoracic and abdominal tumors patients.The accuracy of the four typical prediction algorithms,including ANFIS,neural network (NN),support vector machine (SVM),and CyberKnife Synchrony,was compared by retrospective offline analysis.Results The ANFIS algorithm showed better performance than NN,SVM and Synchrony in normalized root mean square error (nRMSE),the maximum error (Max),and the number of errors greater than 1 mm.Conclusions The accuracy and robustness of the ANFIS algorithm are superior to NN,SVM and CyberKnife Synchrony.The ANFIS can better predict irregular respiratory signals.

Sistema computacional para auxílio ao diagnóstico em exames de tuberculose animal / A computer software system to assist in the diagnosis of animal tuberculosis

The results obtained in evaluating the efficiency of a Neuro-Fuzzy System NEFCLASS (Neuro-Fuzzy Classification) in image classification of cattle tuberculosis, based on its texture features extracted using the wavelet transform are presented. For testing, images of animal tissues diagnosed with tuberculosis were used, as provided by the Tuberculosis Laboratory at the Instituto Biológico de São Paulo. The results of this study can serve as a basis for developing systems for diagnosis aimed at reducing human effort, by automating all or parts of the classification of images, helping lab technicians to sort amongst different pathologies.

Modelo híbrido de aprendizagem utilizando expressões linguísticas e numéricas no auxílio ao diagnóstico / Hybrid model of learning using linguistic expressions and numerical in the diagnosis

O objetivo desta pesquisa é a análise de modelos de aprendizagem, utilizando diferentes operações aritméticas aplicadas de Sistemas Neuro-Fuzzy (NFS)...