Computer Interactive System for Diagnosis, Testing, and Rehabilitation of Visual Perception Disorders

Ruseckaite, Rasa; Raškinis, Gailius; Lukauskienė, Rita

doi:10.1068/v970229

Cited by 2 publications

(2 citation statements)

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“…Researchers have developed several intelligent approaches for medical diagnosis systems in an attempt to identify disease category, reduce waiting time of patients, reduce health care service costs and increase service rate of medical experts. As seen in most studies (Bourlas et al, 1999;Alexopoulos et al, 1999;Ruseckaite, 1999;Manickam and Abidi, 1999;Zelic et al, 1999), intelligent approaches developed to assist experts in timely detection and prevention of diseases can only deal with one disease condition. Hence, it is important to develop a multi-target disease diagnosis system for the identification of two or more disease conditions in patients.…”

Section: Related Workmentioning

confidence: 99%

A decision support system for multi-target disease diagnosis: A bioinformatics approach

Ayo¹,

Awotunde²,

Ogundokun³

et al. 2020

Heliyon

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Malaria and typhoid fever are revered for their ability to individually or jointly cause high mortality rate. Both malaria and typhoid fever have similar symptoms and are famous for their coexistence in the human body, hence, causes problem of under-diagnosis when doctors tries to determine the exact disease out of the two diseases. This paper proposes a Bioinformatics Based Decision Support System (BBDSS) for malaria, typhoid and malaria typhoid diagnosis. The system is a hybrid of expert system and global alignment with constant penalty. The architecture of the proposed system takes input diagnosis sequence and benchmark diagnosis sequences through the browser, store these diagnosis sequences in the Knowledge base and set up the IF-THEN rules guiding the diagnosis decisions for malaria, typhoid and malaria typhoid respectively. The matching engine component of the system receives as input the input sequence and applies global alignment technique with constant penalty for the matching between the input sequence and the three benchmark sequences in turns. The global alignment technique with constant penalty applies its pre-defined process to generate optimal alignment and determine the disease condition of the patient through alignment scores comparison for the three benchmark diagnosis sequences. In order to evaluate the proposed system, ANOVA was used to compare the means of the three independent groups (malaria, typhoid and malaria typhoid) to determine whether there is statistical evidence that the associated values on the diagnosis variables means are significantly different. The ANOVA results indicated that the mean of the values on diagnosis variables is significantly different for at least one of the disease status groups. Similarly, multiple comparisons tests was further used to explicitly identify which means were different from one another. The multiple comparisons results showed that there is a statistically significant difference in the values on the diagnosis variables to diagnose the disease conditions between the groups of malaria and malaria typhoid. Conversely, there were no differences between the groups of malaria and typhoid fever as well as between the groups of typhoid fever and malaria typhoid. In order to show mean difference in the diagnosis scores between the orthodox and the proposed diagnosis system, t-test statistics was used. The results of the t-test statistics indicates that the mean values of diagnosis from the orthodox system differ from those of the proposed system. Finally, the evaluation of the proposed diagnosis system is most efficient at providing diagnosis for malaria and malaria typhoid at 97% accuracy.

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Section: Related Workmentioning

confidence: 99%

A decision support system for multi-target disease diagnosis: A bioinformatics approach

Ayo¹,

Awotunde²,

Ogundokun³

et al. 2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…Many intelligent system have been developed for the purpose of enhancing health-care and provide a better health care facilities, reduce cost and etc. As express by many studies [28][29][30][31][32][33], intelligent system was developed to assist users and provide early diagnosis and prediction to prevent serious illness. Even though the system is equipped with "human" knowledge, the system will never replace human expertise as human are required to frequently monitor and update the system' s knowledge.…”

Section: Review Of Machine Learning In Medicinementioning

confidence: 99%

Deep Learning in Medicine

Jaiswal¹,

Jaiswal²

2019

IJTSRD

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Computer Interactive System for Diagnosis, Testing, and Rehabilitation of Visual Perception Disorders

Cited by 2 publications

References 0 publications

A decision support system for multi-target disease diagnosis: A bioinformatics approach

A decision support system for multi-target disease diagnosis: A bioinformatics approach

Deep Learning in Medicine

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