Neural Nework-based Mobile App Framework to Aid Resource-poor Setting Community Health

Pasha, Muhammad Fermi; Lee, Hui San; Widhiasi, Arini; Purba, Ronsen; Mansour, Ahmed; Budiarto, Rahmat

doi:10.1109/iccit-144147971.2020.9213727

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…But, the poor people living in rural areas have limited healthcare resources. Pasha et al [110] designed a neural network-based mobile app that has pretrained disease predictions for users. Chiu et al [96] proposed an interactive mobile app for self-supervised health management along with a feedback system that increases the likelihood of people becoming healthier.…”

Section: Mhealthmentioning

confidence: 99%

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

et al. 2022

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There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

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Section: Mhealthmentioning

confidence: 99%

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

et al. 2022

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“…Different training dataset models with the same type of hyperparameters. Many studies have proposed model selection methods and approaches [13], [14]. However, the proposed approach focuses on only one or two datasets.…”

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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“…Advances in mobile technology have also motivated researchers to focus on developing mobile systems that can improve stand-alone interactive family discovery systems and make them more accessible, especially in developing countries (Pasha et al, 2020). Furthermore, mobile technology has enhanced ubiquitous interaction among non-coresident family members (Taipale, 2019) also introduced the concept of "digital families" as a type of distributed extended family comprised of related individuals living in one or more households who use at least a basic level of information and communication technologies and social media applications to stay connected and maintain a sense of unity.…”

Section: List Of Tablesmentioning

confidence: 99%

Mobile-based application for discovering family relationship using rule based system in Tanzania

Kipendaroho

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Family is a basic unit in society where traditionally, parents raise their children and family bonds survive longer and provide a primary sense of belonging. It has been observed that it become difficult when extended family become complex to find distant relatives using traditional approaches this is due to rural-urban migration and residential mobility, which has been weakened family relationship. Several researchers have developed systems to help relatives in discovering their family relationships using genealogical data, nevertheless, these systems may provide false-positive findings when there is lack of information. In Tanzania the genealogical sites have insufficient individual’s family information for family discovery. The purpose of this paper is to present a developed mobile based application for discovering family relationships with no use of genealogical data and use of rule-based system to identify the type of relationship with a person. Both primary and secondary data collection methods were used to collect data and analyzed using R-studio. The research revealed that rule-based system can easily discover family relationships, and families grow when people interact with the mobile application (MyFam). The system was validated with users, where results emphasized its efficiency as a discovery tool with performance of 40% in user experience, 40% in system functionalities and 55% in system interface. The contribution of this study is to provide a mobile application that can be used in many countries for discovering family members relationships. In addition, due to efficacy of the rule-based system, any relationship can be inferred simply and reliably based on how family relationships are named.

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Neural Nework-based Mobile App Framework to Aid Resource-poor Setting Community Health

Cited by 3 publications

References 10 publications

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

Neural network models selection scheme for health mobile app development

Mobile-based application for discovering family relationship using rule based system in Tanzania

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