Cognitive Neuro-Fuzzy Expert System for Hypotension Control

Hypotension; also known as low blood sugar affect gender of all sort; hypotension is a relative term because the blood pressure normally varies greatly with activity, age, medications, and underlying medical conditions.  Low blood pressure can result from conditions of the nervous system, conditions that do not begin in the nervous system and drugs. Neurologic conditions (condition affecting the brain neurons) that can lead to low blood pressure include changing position from lying to more vertical (postural hypotension), stroke, shock, lightheadedness after urinating or defecating, Parkinson's disease, neuropathy and simply fright. Clinical symptoms of hypotension include low blood pressure, dizziness, Fainting, clammy skin, visual impairment and cold sweat. Neuro-Fuzzy Logic explores approximation techniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditional procedure of the medical diagnosis of hypotension employed by physician is analyzed using neuro-fuzzy inference procedure. The proposed system which is self-learning and adaptive is able to handle the uncertainties often associated with the diagnosis and analysis of hypotension. Keywords: Neural Network, Fuzzy logic, Neuro Fuzzy System, Expert System, Hypotensio

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases

The largest vertebrate viruses known, infecting humans, and other vertebrates are poxviruses including cowpox, vaccinia, variola (smallpox), and monkeypox viruses. Monkeypox was limited to the rain forests of central and western Africa until 2003. A smallpox-like viral infection caused by a virus of zoonotic origin, monkeypox belongs to the genus Orthopoxvirus, family Poxviridae, and sub-family Chordopoxvirinae. Monkeypox has a clinical presentation like ordinary forms of smallpox, including flulike symptoms, fever, malaise, back pain, headache, and characteristic rash. In view of the eradication of smallpox, such symptoms in a monkepox endemic region should be carefully diagnosed. The problem in diagnosing monkeypox lies in the fact that it is clinically indistinguishable from other pox-like illnesses making virus differentiation difficult. In this paper, we present a neuro-fuzzy based model for early diagnosis of monkeypox virus with a differentiation from other pox families

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic

Fuzzy Decision Tree-based Inference System for Liver Disease Diagnosis

Medical diagnosis can be challenging because of a number of factors. Uncertainty in the diagnosis process arises from inaccuracy in the measurement of patient attributes, missing attribute data and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. Given this situation, a decision support system, which can help doctors come up with a more reliable diagnosis, can have a lot of potential. Decision trees are used in data mining for classification and regression. They are simple to understand and interpret as they can be visualized. But, one of the disadvantages of decision tree algorithms is that they deal with only crisp or exact values for data. Fuzzy logic is described as logic that is used to describe and formalize fuzzy or inexact information and perform reasoning using such information. Although both decision trees and fuzzy rule-based systems have been used for medical diagnosis, there have been few attempts to use fuzzy decision trees in combination with fuzzy rules. This study explored the application of fuzzy logic to help diagnose liver diseases based on blood test results. In this project, inference systems aimed at classifying patient data using a fuzzy decision tree and a fuzzy rule-based system were designed and implemented. Fuzzy decision tree was used to generate rules that formed the rule-base for the diagnostic inference system. Results from this study indicate that for the specific patient data set used in this experiment, the fuzzy decision tree-based inferencing out performed both the crisp decision tree and the fuzzy rule-based inferencing in classification accuracy

Design Methodology of Fuzzy Expert System for the Diagnosis and Control of Obesity

Both developed and developing nations of the world have overtime experienced enormous increase in food and other consumables production. This has led to a rise in calorie intake by people living in these nations of the world. As calorie intake increases in the human system, lack of early detection or control leads to obesity. The study of obesity is gaining utmost importance because of the major health issues associated with it. If an obese prone patient is detected early enough, then quite a number of diseases can be prevented. The ability of fuzzy logic to reason with uncertain and imprecise data in addressing the specific problem of diagnosis and monitoring of diseases in our society cannot be over emphasized. In this paper we design methodology of fuzzy expert system to diagnose and monitor obesity in persons at early stage. The study will help reduce to a great minimum the fast rise of obesity in our society and the world at large. The proposed study is validated with MatLab, and is used as a tracking system with accuracy and robustness. Keywords: Obesity, Fuzzy Inference System, Body Mass Index, Body fat, Waist circumference