Predictive Methodology for Diabetic Data Analysis in Big Data

Kumar, N. M. Saravana; Eswari, T.; Sampath, P.; Lavanya, S.

doi:10.1016/j.procs.2015.04.069

Cited by 132 publications

(22 citation statements)

References 2 publications

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“…There are many papers, working on building classification models like Saravana Kumar et al [9] by using analyzing algorithms over the Hadoop framework for the type of diabetes, entanglement linked with it, and the preferred treatment for the patient. This analysis provides a perfect way to care for diabetics.…”

Section: Diabetes Patient Monitoringmentioning

confidence: 99%

Diabetes classification application with efficient missing and outliers data handling algorithms

Torkey

Ibrahim

Hemdan

et al. 2021

Complex Intell. Syst.

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Communication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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Section: Diabetes Patient Monitoringmentioning

confidence: 99%

Diabetes classification application with efficient missing and outliers data handling algorithms

Torkey

Ibrahim

Hemdan

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

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“…In addition to the aforementioned features of big data, many researchers have added new features due to the numerous numbers of applications available. Indeed, the initial 3Vs proposal has been extended to 4Vs [3], [26], [42], [50], 5Vs [33], [39], 6Vs [40], 7Vs [24] and 10Vs [18], [36]. An updated list of the commonly adopted Vs is presented in Table 1.…”

Section: Big Data: Definition and Characteristicsmentioning

confidence: 99%

“…Moreover, in order to reduce the healthcare costs due to high rates of patients that suffer from chronic diseases, such as diabetes, doctors use big data predictive analytics to foresee high-risk patients and to offer to them customized care [42]. To ensure a therapeutic follow-up for asthmatic patients, an American company named Asthmapolis has developed sensors that are positioned in the top of an inhaler in order to track and follow the inhaler usage.…”

Section: B Healthcare Predictionmentioning

confidence: 99%

BIG DATA for Healthcare: A Survey

et al. 2019

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Recently, the massification of new technologies, which has been adopted by a large majority of the world population, has accumulated a tremendous amount of data, including clinical data. This clinical data have been gathered up and interpreted by medical organizations in order to gain insights and knowledge useful for clinical decisions, drug recommendations, and better diagnoses, among many other uses. This paper highlights the enormous impacts of big data on medical stakeholders, patients, physicians, pharmaceutical and medical operators, and healthcare insurers, and also reviews the different challenges that must be taken into account to get the best benefits from all this big data and the available applications. INDEX TERMS Big data, big data analytics, healthcare.

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“…Big data analytics aid healthcare through predictive analysis. Caregivers use big data analytics to foresee high-risk patients that are vulnerable to chronic diseases in order to provide them the care they need before the health issue becomes an unmanageable problem (Kumar, Eswari, Sampath and Lavanya, 2015). For instance, in cases of asthma attacks, Data related to the place and time of inhaler's use is collected in real ISSN: 2581-3358 Available online at Journals.aijr.in Edet et al, Adv.…”

Section: Impact Of Big Data On Healthcare Servicesmentioning

confidence: 99%

Prospects and Challenges of Population Health with Online and other Big Data in Africa: Understanding the Link to Improving Healthcare Service Delivery

Edet

Afolabi

2019

Adv. J Social Sci.

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Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by big data analytics and the prospects that big data opens in health care services in Africa. The systematic review examined the key research questions to address whether big data applications can improve healthcare service delivery in Africa especially during epidemics or health crises and through the population health system. The paper examined prospects and challenges that are associated with the use of big data and healthcare service in relation to population health needs through influencing factors. In this study, literatures are reviewed to present cases of big data applications in healthcare in Africa and to understand the prospect and challenges of such applications to population health.

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Predictive Methodology for Diabetic Data Analysis in Big Data

Cited by 132 publications

References 2 publications

Diabetes classification application with efficient missing and outliers data handling algorithms

Diabetes classification application with efficient missing and outliers data handling algorithms

BIG DATA for Healthcare: A Survey

Prospects and Challenges of Population Health with Online and other Big Data in Africa: Understanding the Link to Improving Healthcare Service Delivery

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