Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)

Owoseni, Alaba T.; Ogundahunsi, Isaac O.

doi:10.5815/ijieeb.2016.02.02

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The system needs to be enhanced to achieve the error-free system since it has to do with human life. Another system developed in the different platform is the work of [13]. They developed a mobile-based system that diagnoses Malaria using Fuzzy Expert System.…”

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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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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“…Varying numbers of symptoms have been used in these studies, e.g. 12 symptoms [11], 22 symptoms [12], 11 symptoms [13,14], 4 symptoms [15], and 8 symptoms [16]. Despite their contribution to malaria diagnosis research, these studies do not specifically identify significant or important symptoms, let alone non-symptom-related factors.…”

Section: Introductionmentioning

confidence: 99%

Significant Symptoms and Non-Symptom-Related Factors for Malaria Diagnosis in Endemic Regions of Indonesia

Bria

Yeh

Bedingfield

2020

Preprint

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Background: In rural areas of Indonesia, where parasitological tests are not always available, a knowledge of significant symptoms and non-symptom-related factors is crucial for clinical diagnosis of malaria in order to take appropriate actions for treatment. Malaria diagnosis at early stages can avoid a progression in severity of the conditions and possible transmission of the disease. With insufficient knowledge of significant malaria symptoms and non-symptom-related factors for clinical diagnosis of malaria, medical doctors may presumptively treat febrile patients, which leads to an increase in resistance to antimalarial drugs.Methods: The study involves five stages. First, medical records are collected from patients suffering from malaria and other febrile diseases such as dengue fever, typhoid fever, respiratory tract infection, dyspepsia, pneumonia, common cold and gastritis. The records are obtained from two public hospitals in endemic regions in Indonesia’s East Nusa Tenggara Province, East Sumba and Lembata. Second, interviews with eight Indonesian medical doctors are conducted to understand the symptoms and non-symptom-related factors they use for clinical diagnosis of malaria. Third, symptoms and non-symptom-related factors from 472 collected medical records are selected and ranked using the correlation matrix method. Fourth, the 17 feature sets extracted from the ranked features are used to develop and compare various malaria classifiers using three machine-learning techniques, including logistic regression, support vector machines and k-nearest neighbors. Fifth, the most significant symptoms and non-symptom-related factors are identified for malaria diagnosis using the malaria classifiers developed. In addition, further experiments and comparisons are conducted to examine the performance of malaria classifiers using various feature sets.Results: (a) Out of 17 features identified from medical records, six significant symptoms (duration of fever, headache, nausea and vomiting, heartburn, pain and severe symptoms), and two non-symptom-related factors (history of malaria and age) contribute most to malaria diagnosis. These significant features can be used for malaria diagnosis with an average 85% accuracy. (b) As a symptom, duration of fever is more significant than temperature or fever for distinguishing malaria from other febrile diseases. (c) Trios malaria symptoms (fever, shivering and sweating) are shown to be less significant than other symptoms in endemic regions.Conclusions: The findings of this study can be used to raise the public awareness of significant malaria symptoms and non-symptom-related factors within societies vulnerable to malaria. These findings can also be used for helping unexperienced medical doctors with clinical diagnosis of malaria especially in rural areas, where the availability of parasitological tests is scarce.

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“…This is AI rule-based reasoning (fuzzy based reasoning) system developed using techniques such as fuzzy logic and fuzzy genetic algorithm [8], [9], [10]. This fuzzy based reasoning is one approach to construct domain knowledge.…”

Section: Mental Health Diagnostic Expert System (Mehdes)mentioning

confidence: 99%

Smart Virtual Expert System to Assist Psychiatrists (SVESTAP)

Arachchillage¹

2018

IJITCS

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Abstract-Psychological issues in the world are exponentially growing and the treatment gap is also comparatively high. The main reason would be the shortage of expertise and time-consuming in conventional diagnose process. The main objective of this research is to lower the mental issues treatment gap of professionals or apprentices in the field by creating a virtual expert system to assist psychiatrists. This system diagnoses most common mental disorders such as Depression Disorder, Anxiety Disorder, and Dementia. The proposed expert system can communicate with patients, to identify the current state of the illness. During the conversation, a standard questionnaire is given for the disease verification purpose. The experienced mental health professionals can use this expert system to assist in diagnosing process and the apprentices of the psychology can use this expert system as a training asset.

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Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)

Cited by 6 publications

References 5 publications

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

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

Significant Symptoms and Non-Symptom-Related Factors for Malaria Diagnosis in Endemic Regions of Indonesia

Smart Virtual Expert System to Assist Psychiatrists (SVESTAP)

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