Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer

Safdari, Reza; Arpanahi, Hadi Kazemi; Langarizadeh, Mostafa; Ghazisaiedi, Marjan; Dargahi, Hossein; Zendehdel, Kazem

doi:10.5455/aim.2018.26.19-23

Cited by 21 publications

(11 citation statements)

References 4 publications

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“…Fuzzy logic was first described in 1965, it is a tool that produces a mechanism for reply to imprecise information based in qualitative language and transcribed to a numerical language (Boolean logic). Thus, fuzzy logic has been used indeed in medicine to diagnose the diseases based clinical and imaging data (13,14) . The fuzzy logic has been used in clinical research, especially in diabetes and your complications as DR, diabetic neuropathy, and diabetic nephropathy (15,16) .…”

Section: Discussionmentioning

confidence: 99%

Fuzzy logic use in classification of the severity of diabetic retinopathy

Andrade¹,

França

Andrade

et al. 2020

Preprint

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Purpose: Employ fuzzy logic to auxiliary in identification and diagnosis the gravity of diabetic retinopathy (DR). Methods: A cross-sectional study was performed, being assessed 100 diabetes mellitus patients with DR. The following ultrasound findings were measured employing a semi-quantitative punctuation method: vitreous hemorrhage, posterior vitreous detachment, epiretinal fibrosis, retinal detachment. The fundus photography (FP) aspects evaluated for diagnosis of DR were at least four or more microaneurysms with or without hard or soft exudates, and neovascularization, graded using the Early Treatment of Diabetic Retinopathy Scale. With the combination between ultrasound punctuation and FP aspects through fuzzy logic, a classification for DR has been built. Results: Microaneurysms were the findings which presented the better interaction with the DR severity on ultrasound, while the hard exudates showed the minors estimation errors when compared to soft exudates. A classification for DR was suggested based on the 95% confidence interval of number of microaneurysms: mild group (< 24.6); moderately mild (24.6 - 48.0); moderate (48.1 - 64.5); moderately severe (64.6 - 77.0); severe (77.1 - 92.7); and very severe (> 92.7). Conclusion: By the fuzzy logic, a DR classification was constructed supported on number of microaneurysms measurement with a simple practical application. Keywords: Fuzzy logic; Ultrasound imaging; Fundus retinal image; Diabetic retinopathy; Diabetes mellitus.

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Section: Discussionmentioning

confidence: 99%

Fuzzy logic use in classification of the severity of diabetic retinopathy

Andrade¹,

França

Andrade

et al. 2020

Preprint

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“…In this context, fuzzy logic has been identified as a substantial tool that is used to model human decision-making of an expert in a computer program such that the program can mimic the expert’s ability to solve problem [ 65 ]. The ability of fuzzy logic to handle imprecise and uncertain situations makes it the appropriate approach to realize medical diagnoses [ 66 , 67 ].…”

Section: Our Framework For Detecting and Analyzing Behavior Changementioning

confidence: 99%

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

Zekri

Delot

Thilliez

et al. 2020

Sensors

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A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

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“…Data mining is the process of selecting, discovering, and modeling huge volumes of data for extracting concealed patterns or potential relationships that provide valuable information. Mining healthcare data could improve medical evaluation screening, prognosis, diagnosis, treatment, and survival leading to enhanced clinical decision-making (13,14).…”

Section: Introductionmentioning

confidence: 99%