Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model

Ajenaghughrure, Ighoyota Ben; Sujatha, P.; Maureen, Akazue

doi:10.5815/ijitcs.2017.10.02

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…1 Fuzzy logic system [17] Despite the fact that FL reasons on a higher level using linguistic variables like that of humans, it still has concerns arising from the inability to learn properly, though appreciably good in other areas like imprecision tolerance, explanation ability and knowledge representation [13]. Several researchers have recently used the concept of FL to model intelligence into medical applications for diagnosis of ailments such as Lassa fever [18], [19], Lung Cancer [20], Hepatitis B [21]- [23], Typhoid Fever [24], HIV [25], Back pain [26], Arthritis [27], Breast Cancer [28], Diabetes [29], [30], Pelvic Inflammatory Disease [31], Leprosy [32], Multi-Fever [33], Medical Diagnosis [34], Diseases classifier [35], Asthma and Chronic Obstructive Pulmonary Disease [36]- [38], Prostate cancer [39], Peptic Ulcer [40], Multiple Sclerosis [41], Gonorrhea [42], Enteric Fever [43], Cat Anal Gland Cancer [44].…”

Section: Fuzzy Logicmentioning

confidence: 99%

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

2020

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Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients.

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Section: Fuzzy Logicmentioning

confidence: 99%

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

2020

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“…These studies diagnosed different diseases with different platform and techniques with different accuracy result. [8] designed a web-based expert multi-fever diagnosis system using a novel fuzzy symptom classifier with human self-observed physiological symptoms. It diagnoses common types of fever widely reported with scientifically available medication which includes malaria, dengue, typhoid, Lassa and yellow fever with the use of Fuzzy Logic.…”

Section: Related Workmentioning

confidence: 99%

MeDevice: A Mobile – Based Diagnosis of Common Human Illnesses using Neuro – Fuzzy Expert System

Lidasan¹,

Tagacay²

2019

IJITCS

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Fever is a sign that the body is trying to fight infection. It is usually accompanied by various sicknesses or symptoms that signal another illness or disease. Diagnosing it ahead of time is essential because it has to do with human life and to determine what to do to get well. MeDevice is a mobile-based application that runs in Android devices that allows the user to enter the levels of his/her symptoms and diagnoses the disease either as influenza, dengue, chicken pox, malaria, typhoid fever, measles, Hepatitis A and pneumonia together with its details and its first aid treatment. It aims at providing an efficient decision support platform to aid people with fever in diagnosing their disease and whether or not to seek medical attention especially in developing countries like the Philippines. This application is engineered with the knowledge base and the inference method of fuzzy logic and expert system with the help of Gradient Descent optimization algorithm and back propagation neural network to achieve the optimum value of the error rate. This is essential to provide the application with a high accuracy rate which shows during the conduct of testing of the application.

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“…For this reason, the use of expert systems is increasing and also being used in a number of sectors of our social and technological life. In fact, in those the use of expert systems is becoming quite a significant aspect during problem solving and decision support [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Designing a Rule Based Expert Systems for Contact Lenses Patients

Aydilek¹,

Gümüşçü²

2018

IJITCS

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Abstract-Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.

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Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model

Cited by 8 publications

References 10 publications

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

MeDevice: A Mobile – Based Diagnosis of Common Human Illnesses using Neuro – Fuzzy Expert System

Designing a Rule Based Expert Systems for Contact Lenses Patients

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