A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

Isinkaye, Folasade Olubusola; Soyemi, Jumoke; Oluwafemi, Olayinka P.

doi:10.5815/ijieeb.2017.06.03

Cited by 5 publications

(8 citation statements)

References 14 publications

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“…During the epidemic, people across the country will need to wear masks. Therefore, face recognition systems during an epidemic must be improved in terms of functionality and key technologies compared to traditional face recognition [4].…”

Section: The Need For Technological Improvements In Face Recognition ...mentioning

confidence: 99%

A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

Deng

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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At present, "COVID-19" has spread in 184 countries (regions) in the world, and more than 500 million patients have been diagnosed and caused 66.3 million deaths. The worldwide epidemic is still ongoing, and the epidemic in China is still increasing, therefore, the regular prevention and control of the epidemic is An important part of ensuring people live, live, live, especially in crowded places, there is an urgent need for artificial intelligence, biometrics and other technologies. This paper proposes the face recognition method of sparse enhancement and collaborative information fusion. The paper first explains and generalises the relevant terms, and finally, the practicality of the algorithm is demonstrated through experimental analysis. The recognition rate of the algorithm described in this paper is generally higher than 95% on different facial masking ratios, and the results show that the face recognition method of sparse enhancement and collaborative information fusion based on epidemic prevention and control is a very worthy research The results show that face recognition based on sparse enhancement and collaborative information fusion for epidemic prevention and control is a very worthwhile research technique that can help solve the critical problems of epidemic prevention and control today.

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Section: The Need For Technological Improvements In Face Recognition ...mentioning

confidence: 99%

A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

Deng

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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“…Some research studies proposed cost effective ML methods to predict heart disease, the vision being to help reduce the cost incurred by the patients. Isinkaye et al [11] proposed a mobile based neuro fuzzy model for cardiovascular disease diagnosis. It was noted that although the proposed method was cost effective, the results were inadequate.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Approach for Classification Problem Case Study: Heart Disease Classification

Khawaja¹,

Low²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Heart disease is a major cause of death globally, with patients succumbing to death a few years of being diagnosed. This paper proposed a novel hybrid approach of Cuckoo Search Optimization -Extreme Learning Machine (CSO -ELM) to solve a classification problem. The approach was compared with established models proven in classifying heart disease. The CSO-ELM indicated significant predictive ability and outperformed the established and base models in machine learning.

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“…It enables patients and clinicians to easily access Internet health resources. mHealth can also be considered an educational app, a patient information and documentation tool, a patient monitoring app, a decision support tool, and a patient support tool [11], [12]. Many mHealth apps already exist in Malaysia and Indonesia, including myHealth, myMaHTAS, iDengue, Halodoc, Alodokter, KlikDokter, Medi-Call and SehatQ.…”

Section: Introductionmentioning

confidence: 99%

Neural network models selection scheme for health mobile app development

Triana

Osman²,

Pratomo³

et al. 2023

IJ-AI

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Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86.00% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.

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A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

Cited by 5 publications

References 14 publications

A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

A Novel Hybrid Approach for Classification Problem Case Study: Heart Disease Classification

Neural network models selection scheme for health mobile app development

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