A Fuzzy Inference System to Support Medical Diagnosis in Real Time

Medeiros, Igor Barone de; Machado, Maria Augusta Soares; Damasceno, Wallace José; Caldeira, André Machado; Santos, Rodrigo Costa dos; Filho, Joel Batista da Silva

doi:10.1016/j.procs.2017.11.356

Cited by 32 publications

(15 citation statements)

References 2 publications

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“…This way, the users can take better decisions according to the risk and uncertainty of the system. This is why this method is referred (Das et al 2020) 2020 Medical disease analysis Feature Extraction Model using Neuro-Fuzzy for classification (Tiwari et al 2020) 2020 Lung Cancer Fuzzy Inference System for detection of lung cancer (Kour et al 2020) 2020 Medical disease analysis Neuro-fuzzy systems for prediction and classification of different types of diseases (Vidhya & Shanmugalakshmi, 2020) 2020 Medical disease analysis Modified-ANFIS using various disease analysis based on medical Big Data (Ranjit et al 2020) 2020 Knee Diseases Knee Diseases Prediction using adaptive and improved ANFIS (Hekmat et al 2020) 2020 Acute kidney Injury Risk factors, prevalence, and early outcome analysis of acute kidney injury (Sood et al 2020) 2020 dengue fever LDA-ANFIS based dengue fever risk assessment framework (Sujatha et al 2020) 2020 Breast cancer Micro calcifications in breast identification utilizing ANFIS (Liu et al 2019) 2019 Prostate Cancer Using a fuzzy inference system, prostate cancer was predicted (Turabieh et al 2019) 2019 Breast cancer A D-ANFIS is used to handle the missing values in the application used for the Internet of Medical Things (Mori et al 2019) 2019 Medical decision making Extracting the relationship between input and output of the learning data using fuzzy rules (de Medeiros et al 2017) 2017 Medical decision making Real-time medical diagnosis using a fuzzy inference system (Nguyen et al 2015) 2015 Breast cancer A new classifier based on the type-2 fuzzy logic system for breast cancer diagnosis (Azar & Hassanien, 2015) 2014 Breast cancer Medical big data dimensionality reduction using a neuro-fuzzy classifier (Papageorgio...…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Section: Monte Carlo Simulationmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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“…This helps in creating a foundation of health resources for hospital administrators depending on the medical detection procedure. 6 The fuzzy rule-based system was developed to identify the risk parts of diabetes. From the patient's clinic audit report, the system was able to classify the risk into three levels: low, medium and high.…”

Section: Related Workmentioning

confidence: 99%

A proposed health monitoring system using fuzzy inference system

Ghosh

Roy

Merdji

2020

Proc Inst Mech Eng H

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Due to the busy schedule of every human being in today’s world, consciousness towards one’s health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to be under control. Because of heavy work pressure, anxiety, change of weather and location or due to some other causes, the effect of the diseases can turn up into an appalling state. Two vital aspects of human diseases are blood pressure (hypertension) and blood sugar imbalance. Hypertension is one of the complications of prolonged untreated diabetes. Other organs like kidney, eye and peripheral nerves are also involved. The various gradations of hypertension and diabetes are required to understand for the progression of the disease and to make plan for the treatment. So these two aspects are considered in this article. The idea is to develop a logic, which could be incorporated in a pocket friendly device in future that would generate an alarm whenever there is imbalance in blood sugar or blood pressure levels. The concept of the fuzzy inference rule and first-order logic is implemented to develop this study.

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“…Fuzzy logic is a powerful tool for representing and handling this uncertainty, leading to fuzzy systems that can support decisions in medical diagnosis. Fuzzy logic has been widely used for medical diagnosis due to its capacity to express, in a formal way, approximate concepts and reasoning, which strongly characterize the medical field [25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Specifically, using fuzzy logic, the knowledge of the medical expert can be easily formalized in terms of linguistic fuzzy rules [39].…”

Section: Risk Assessment By Fuzzy Rulesmentioning

confidence: 99%

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

et al. 2018

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Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

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A Fuzzy Inference System to Support Medical Diagnosis in Real Time

Cited by 32 publications

References 2 publications

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

A proposed health monitoring system using fuzzy inference system

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

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