WITHDRAWN: Health data analytics: Framework &amp; review on tool &amp; technology

Ravikumaran, P.; Devi, K. Vimala; Kartheeban, K.; Prasanth, N. Narayanan

doi:10.1016/j.matpr.2020.10.131

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…These vast amounts of data gathered from interactions between patients and healthcare professionals would aid in lowering costs and raising the standard of service. Additionally, it aids in disease surveillance, community health management, and clinical decision support [4].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Predictive - Diagnosis of Prevalent Illness among University Students

Isiaka

Agbogun

Kolajo

2022

J. Appl. Artif. Intell.

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The issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbors, and linear regression machine learning models, experimental findings revealed that it outperformed current methods. In comparison to other reviewed frameworks that used survey datasets, standardized or online repositories' dataset, the framework with emphasis on the ensemble Gradient Boosting classifier and regression had accuracy of 100% and mean absolute error of 0.18, respectively. It is also steady due to its ability to manage both small and big data sets without impacting the model's performance. The enhanced results through localized dataset demonstrate the benefit of including local data sources in the process of developing models for the diagnosis and prognosis of prevalent illnesses of any area with people.

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Section: Introductionmentioning

confidence: 99%

A Framework for Predictive - Diagnosis of Prevalent Illness among University Students

Isiaka

Agbogun

Kolajo

2022

J. Appl. Artif. Intell.

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Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

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Real Time Poisoning Attacks and Privacy Strategies on Machine Learning Systems

Kartheeban

2024

2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL)

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WITHDRAWN: Health data analytics: Framework & review on tool & technology

Cited by 5 publications

References 10 publications

A Framework for Predictive - Diagnosis of Prevalent Illness among University Students

A Framework for Predictive - Diagnosis of Prevalent Illness among University Students

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Real Time Poisoning Attacks and Privacy Strategies on Machine Learning Systems

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