Using Big Data Technology to Contain Current and Future Occurrence of Ebola Viral Disease and Other Epidemic Diseases in West Africa

Ayeni, Foluso; Misra, Sanjay; Omoregbe, N. A.

doi:10.1007/978-3-319-20469-7_13

Cited by 7 publications

(10 citation statements)

References 4 publications

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“…The latter does not come with a file system other than Hadoop, so you need to combine it with a different file system based on the cloud [53]. Hadoop spends time running computationally complex machine learning algorithms [54,55], making Apache Spark 100 times faster than ever before. Also, trends and patterns which make it easy to diagnose and treat patients are been revealed by big data.…”

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos' parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.

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Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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“…Another basic preprocessing operation is discretization, which reduces the number of values a feature takes on. For example, age can be in a range from 0 to 110 years but can be discretized by grouping ages into intervals such as infants (0-1 year), children (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), teenagers (13)(14)(15)(16)(17)(18), young adults (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), etc. Discretization is performed with or without taking into account class information (like diagnosis).…”

Section: Medical Data Preprocessingmentioning

confidence: 99%

“…Then it spreads causing outbreaks that are challenging to contain. Analyzing data from previous Ebola outbreaks lead to identification of new animal reservoirs, thus increasing population awareness in high-risk regions [12]. IBM used adaptive models of disease dynamics to accommodate for such properties as virus spreading through direct contact with infected blood or body fluids (i.e., urine, saliva, feces, vomit, and semen), contaminated objects (e.g., needles) and infected animals.…”

Section: Deploymentmentioning

confidence: 99%

Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine

Cios,

Krawczyk,

Cios

et al. 2019

Preprint

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The collection of healthcare data occurs also at social media giant tech corporations [39]. Data collected on all of us, such as by Facebook, can be and are used for dubious purposes like social engineering and marketing, along with potentially good purposes [32]. Unfortunately we have witnessed huge data breaches (Facebook, Yahoo) that make data available to bad players. Some new technologies can alleviate this problem, for instance, using blockchain technology leaves a trace of every transaction performed, such as sending data from one hospital to another, but this works only for official data transfers, as blockchain cannot prevent data from being hacked or stolen. Because of the latter, we shall talk about P5 medicine, by adding the Privacy-preserving attribute to the predictive, preventive, personalized, and participatory. It is a new task, to develop technologies to better protect health data in the future.New technologies and medical data mining. We describe below how new technologies, several of which came into being since publication of our original paper on the uniqueness of medical data mining [25], that collect large-scale heterogeneous medical data on individuals and populations, are changing the field of medicine. Examples of data generated by those technologies are image and omic (genomic, proteomic, etc.) data. Below, we highlight the biggest changes that have occurred since the Cios and Moore paper [25].Human medical data are at the same time the most rewarding and difficult of all biological data to analyze [101]. Humans are the most studied species on earth and data and observations collected on them are unique and cannot easily be gained from animal studies. Examples are visual, cognitive and perceptive data, such as those relating to discomfort, pain and hallucinations. Animal studies, being shorter, cannot track long-term disease processes, such as atherosclerosis. Since the majority of humans have had at least some of their medical information collected in digital form, this translates into big data. Unfortunately, analyzing human data is not straightforward because of the ethical, legal, social and other constraints that limit their use. Hospitals are the ones who store majority of medical data, however, many of them have little interest in sharing them, which adversely impacts global healthcare by restricting large-scale data analysis and new findings. While the current trend is towards open-access data and collaborative environments, issues related to medical data privacy and market competition are not easily alleviated.On the technology side we have seen creation of disruptive technologies such as blockchain, cloud computing, wearable devices, and augmented reality. A disruptive technology does not have to be an entirely new one: often it has been long-existing but was greatly improved in terms of, for instance, speed or accuracy. Examples of the latter technologies are artificial intelligence (AI) and machine learning [9], natural language processing (NLP) [110], image understanding [...

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“…Surveillance data can also be got from human behaviour, which consists of Internet use (web queries, press dispatches, social media, press articles), Telephone (hotlines), Drug sales and Absenteeism. Health Care also provide viable disease surveillance data via Sentinel surveillance (sentinel physicians who agree to notify the public health authorities at regular intervals of patients presenting certain The researchers listed some surveillance strategies such as Disease-specific surveillance, Eventbased surveillance and Syndromic surveillance [26].…”

Section: Traditional and Syndromic Surveillance Of Infectious Diseasementioning

confidence: 99%

Epidemic Alert System: A Web-based Grassroots Model

Osaghae

Okokpujie

Ndujiuba

et al. 2018

IJECE

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Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.

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Using Big Data Technology to Contain Current and Future Occurrence of Ebola Viral Disease and Other Epidemic Diseases in West Africa

Cited by 7 publications

References 4 publications

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine

Epidemic Alert System: A Web-based Grassroots Model

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