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A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The main goal is to model the behavior of blood pressure based on monitoring data o...
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Citations
... More than 580 million people, according to the study, have no idea that their blood pressure is abnormal [80][81][82][83]. Twenty years ago, clinical guidelines for AH management were vague and did not provide any recommendations for screening or treatment; now, however, they do include specific recommendations for particular high-risk categories [84][85][86][87][88]. ML and AI are powerful medical technologies that may aid in the accurate diagnosis of AH, the prediction of when hypertension will start, and the estimation of the possible decrease in cardiovascular events. ...
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
... Based on the 24-hour recording of the patient's blood pressure, Guzman et al. implemented the classification of blood pressure values using a neuro-fuzzy model, in which the rule was optimized with the help of a genetic algorithm [8]. Ali et al. used type-2 fuzzy sets and fuzzy ontology together in their study in patient monitoring, thereby increasing the accuracy of prediction [9]. Miranda et al. proposed a new approach for evaluating the quality of photoplethysmographic (PPG) signals used in wearable cardiology measurement devices. ...
In today's health-conscious world, patient monitoring is increasingly emphasized both in science and in everyday life. For this reason, personalized, reliable evaluation has been in the focus of science in recent years. In this paper, a fuzzy-based system is presented that can handle blurred boundaries when evaluating physiological values. In order to ensure a personalized evaluation in the model, it takes into account statistics prepared from values measured under identical or nearly identical conditions. The essence of the proposed method is to modify the normal range determined on the basis of medical recommendations by the patient's usual reactions. As a consequence of this modification an even more personalized and realistic result can be generated. This modification is based on the use of interval type-2 fuzzy sets to handle the uncertainty arising from the discrepancy between the doctor's recommendation and the patient's normal reactions.
... Based on the above, in order to solve the classification problem in accordance with [3,19,20], all records were divided into three classes according to the value of BP with the assignment of the label Y={0, 1, 2} as shown in Table 1. As a result, Table 2 gives the main statistical indicators of BP parameters for certain classes. ...
Determining the level of blood pressure (BP) in a non-invasive way and without a sphygmomanometer cuff is of great relevance when conducting continuous monitoring or screening studies. In this regard, a method for predicting BP parameters based on the signals of the photoplethysmogram (PPG) and electrocardiogram (ECG) signals has been developed. It is proposed to use, as informative features, the time of pulse wave propagation (PTT) and a set of calculated pulse parameters of PPG. PTT is defined as the time intervals between the R-wave of the ECG and the corresponding characteristic points on the PPG acquired optically from the finger. As parameters of the PPG pulse, the known characteristics of this signal described in the literature are used, as well as additional informative features selected during the study.
In accordance with the above, the tools of machine learning theory were used to construct a classifier model and regression models. The approach described in this paper to determine BP makes it possible to use 10-second ECG signals in any of the 12 common leads and PPG signals from any optical type of sensor.
The built model of the classifier detects three levels of BP: low, normal, and high, at the accuracy metric=0.8494. The regression models predict systolic, diastolic, and mean BP parameters in accordance with the requirements of the British Hypertension Society (BHS) standard by the magnitude of the absolute error.
The proposed method for assessing the level of BP involves real-time measurements and can be used in the design of measuring equipment for screening studies, as well as in continuous monitoring tasks within the framework of BHS requirements.
... This neural network is part of a neuro-fuzzy hybrid model Miramontes et al. 2018b) with which different results related to the blood pressure are obtained; different modules used in the proposed model have been optimized (Miramontes et al. 2018a(Miramontes et al. , 2020a(Miramontes et al. , 2020bGuzmán et al. 2019;Guzman et al. 2017;Carvajal et al. 2021). ...
Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of the C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm’s performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also one of the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.
... The use of intelligent techniques in medical diagnosis is increasingly common, which allows the development of intelligent systems based on knowledge [1,2]. These techniques have performed very well for the diagnosis of many diseases [3,4]. ...
This article presents a fuzzy system for determining the level of infection of four viral diseases: dengue, zika, chikungunya, and yellow fever. Medical diagnosis is a complicated task because it is full of uncertainty and imprecision, which many times causes a misdiagnosis. However, this problem can be treated through a fuzzy system with the ability to work with uncertainty by providing the opportunity to model conditions that are imprecisely defined. Dengue, yellow fever, zika, and chikungunya are diseases that in their early stages are difficult to distinguish since they have great similarities in their symptoms, making diagnosis even more difficult. Based on research, the six most relevant symptoms that allow a better diagnosis have been selected. The six input fields are pain, temperature, bleeding, poor appetite, muscle weakness, and shortness of breath. The four output fields refer to diseases and each one is made up of several fuzzy sets that make it possible to identify the level of infection in each disease. The fuzzy system implementation has been done in MATLAB, using fuzzy rules developed with the help of a human expert. The system is not a substitute for expert medical practitioners, but rather an aid in the diagnosis, which is based on the symptoms experienced by the patient. The tests were carried out in 15 cases with viral diseases and the results show that the diagnosis using the fuzzy system has an accuracy of 86.6%, displaying its helpfulness in the early diagnosis of the disease.
... Likewise, Kalaivani and Sivakumar [26] evaluate as inputs the Systolic Blood Pressure, Heart Rate, and Blood Sugar so as to estimate the patient's Risk Level. Guzman et al. [27] propose the attributes of Systolic and Diastolic Blood Pressure in their neuro-fuzzy hybrid model which is proposed as a new artificial intelligence method to classify blood pressure. A novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm, is described by Moameri and Samadinai [28] by considering the attributes: Age, Chest pain type, Resting blood pressure, Electrocardiographic Results, Maximum Heart Rate, and Cholesterol level. ...
In emergency situations, different actors involved in first aid services should be authorized to retrieve information from the patient’s Electronic Health Records (EHRs). The research objectives of this work involve the development and implementation of methods to characterise emergency situations requiring extraordinary access to healthcare data. The aim is to implement such methods based on contextual information pertaining to specific patients and emergency situations and also leveraging personalisation aspects which enable the efficient access control on sensitive data during emergencies. The Attribute Based Access Control paradigm is used in order to grant access to EHRs based on contextual information. We introduce an ABAC approach using personalized context handlers, in which raw contextual information can be uplifted in order to recognize critical situations and grant access to healthcare data. Results indicate that context-aware ABAC is a very effective method for detecting critical situations that require emergency access to personal health records. In comparison to RBAC implementations of emergency access control to EHRs, the proposed ABAC implementation leverages contextual information pertaining to the specific patient and emergency situations. Contextual information increases the capability of ABAC to recognize critical situations and grant access to healthcare data.
... This neural network is part of a neuro-fuzzy hybrid model [35]- [38] with which different results related to the patient's blood pressure are obtained; different modules used in the proposed model have been optimized [15], [39]- [42]. Fig.12, a representation of the optimization process is presented. ...
Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm's performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses patient information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.
... Later, a multidisciplinary technique has also been applied to artificial swarm intelligence to deal with heterogeneous computing and [40]. In order to carry out the applications of fuzzy logic in the medical field, Guzman [41] proposed an optimized fuzzy qualifier for blood pressure disease. Tyagi, K. and Tyagi, K. published a paper A Comparative Analysis of Optimization Techniques, in which they used various techniques for the test cases of optimization to choose less vague test cases [42]. ...
... Using an ensemble approach, a novel classifying technique has been developed to classify the detected tumor incorporating the extracted features. Guzman et al. (2017) proposed a neuro-fuzzy hybrid model (NFHM) to categorize blood pressure using fuzzy system. The rules provided are optimized by a genetic algorithm to get the most ideal number of principles for the classifier with the least classification error. ...
Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.
... To classify the trends, fuzzy system is used that has rules, and by genetic algorithm, these rules are optimized to obtain the best possible rules for the classifier. 13 With the help of a hybrid method that has fuzzy weighted pre-processing and artificial immune recognition system (AIRS), a unique model has been developed for the diagnosis of heart disease. The robustness of this model was tested using classification accuracy, kfold cross-validation method and confusion matrix. ...
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.
