Fuzzy Logic in Medicine

LaBrunda, Michelle; LaBrunda, Andrew

doi:10.4018/9781605660509.ch174

Cited by 2 publications

(2 citation statements)

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“…Thanks to this advantage, such clustering method could also be employed for other soft computing studies in clinical fields. The present model cannot be illustrated in the form of regressions, graphs or other visual means, yet the integrated fuzzy precision arising from human mentality could produce results akin to the physicians' decisions [14], which is definitely a claim requiring further clinical research. This study could boldly claim to be the first (inside or outside the country) to have employed ANFIS for breast cancer risk assessment.…”

Section: Discussionmentioning

confidence: 97%

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Atashi

Nazeri

Abbasi

et al. 2017

mcij

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Introduction: The Adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study, this model was used for breast cancer detection. Methods: A set of 1508 records of cancerous and non-cancerous participants' risk factors was employed for this study. First, the risk factors were classified into three priorities according to their importance level, were then fuzzified and the subtractive clustering method was used for their input with the same order. Randomly, the dataset was divided into two groups of 70% and 30% of the total records, and used for training and testing the new model, respectively. After the training, the system was separately tested with the Wisconsin and real clinical data, and the results were reported. Results: The desired fuzzy functions were defined for the variables, and the model was trained with the combined dataset. Testing was conducted first with 30% of that dataset, and then with the real data obtained from a real clinical (BCRC) data, while the model's precision for the above stages was 81% (sensitivity = 85.1%, specificity = 74.5%) and 84.5% (sensitivity = 89.3%, specificity = 79.9%) respectively. Conclusions: A final ANFIS model was developed and tested for two standard and real datasets on breast cancer. The resulting model could be employed with high precision for the BCRC Clinic's database, as well as conducting similar studies and re-evaluating other databases.

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Section: Discussionmentioning

confidence: 97%

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Atashi

Nazeri

Abbasi

et al. 2017

mcij

View full text Add to dashboard Cite

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“…FL examines very valuable classical logic in its generalized form and, rather than a definite conclusion, it is an artificial intelligence rule-based form, which uses approximate means of solution and membership functions (25). In this study, a trapezoidal membership function was selected.…”

Section: Fuzzy Logic Classificationmentioning

confidence: 99%

Using fuzzy logic for diagnosis and classification of spasticity

Alcan¹,

Canal²,

Zinnuroğlu³

2017

Turk J Med Sci

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IntroductionSpasticity is a common sensory-motor control disorder characterized by increased velocity-dependent stretch reflex responses resulting from upper motor neuron (UMN) lesions (1). Spasticity is frequently observed in cases such as spinal cord injury, multiple sclerosis, traumatic brain damage, cerebral palsy, and stroke, which can be accompanied by cerebral and spinal pathology that is dispersed or regional (2). The pathophysiology of spasticity is complicated and it is rather difficult to understand the underlying mechanism since it is necessary to know the pathophysiological mechanism that would differentiate the UMN syndrome from other symptoms (3,4). Therefore, spasticity is a disorder that is both insufficiently defined and measured (5). There are three major approaches, clinical, neurophysiological, and biomechanical, for assessing spasticity.The Ashworth Scale (AS) and the modified Ashworth Scale (MAS) are the most commonly used clinical measures of spasticity. The validity, reliability, and sensitivity of the clinical scales, which are subject to interpretation and generally intensified upon passive movement resistance, are debatable (6-8). As a consequence, many studies have been conducted using biomechanical and electrophysiological measurements with the objective of evaluating spasticity, and the obtained results have been used to make correlations with the clinical scales (8-14). Surface electromyography (EMG) has been commonly used in electrophysiological measurements of spasticity (14-16). However, parameters extracted from surface EMG have been generally obtained from analysis by using only the time domain in previous studies, and results were generally based on correlations with the clinical scales rather than using stand-alone surface EMG as well as considering causality. Due to the fact that surface EMG signals are nonstationary and random signals, some pathological data might not be discriminated in the time axis. Therefore, it is possible to utilize the short-time Fourier transform (STFT) and the wavelet transform (WT) methods from the time and the frequency components of the signals found in the pathological symptoms (17,18).Background/aim: Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate measurement and evaluation standards that can explain all aspects of a possible spasticity occurrence. The objective of this study is to develop a fuzzy logic classifier (FLC) diagnosis system, in which a quantitative evaluation is performed by surface electromyography (EMG), and investigate underlying pathophysiological mechanisms of spasticity.Materials and methods: Surface EMG signals recorded from the tibialis anterior and medial gastrocnemius muscles of hemiplegic patients with spasticity and a healthy control group were analyzed in standing, resting, dorsal flexion, and plantar flexion positions. The signals were processed with different methods: by using their amplitudes in the time domain, by applying short...

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Fuzzy Logic in Medicine

Cited by 2 publications

References 0 publications

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Using fuzzy logic for diagnosis and classification of spasticity

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