Image Pattern Recognition for an Intelligent Healthcare System: An Application Area of Machine Learning and Big Data

2019 ◽  
Vol 16 (9) ◽  
pp. 3932-3937 ◽  
Author(s):  
Mohit Chhabra ◽  
Rajneesh Kumar Gujral
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Big Data ◽  
Health Sector ◽  
Medical Application ◽  
Medical Data ◽  
Healthcare Sector ◽  
Medical Expert ◽  
Intelligent Healthcare ◽  
Image Pattern

Today healthcare sector is completely distinguished from other industries. It is a highly important area and people wants highest level of care and facilities irrespective of cost. It could not accomplish social prospect even though it consumes vast fraction of budget. Frequently the analyses of medical data were done by the medical expert. In terms of image analysis by different human expert, it is often restricted due to its subjectivity, image complexity, widespread differences occur across different translators, and fatigue. As after the feat of Big Data and machine learning in real world medical application, it is similarly giving exhilarating results with fine precision for medical imaging and is viewed as an important factor for upcoming applications in area of health sector. This paper presents survey of different applications on the Machine Learning and Big Data which relies on image pattern recognition.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

scholarly journals Diabetes Impacted Cardiovascular Disease Prediction using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4376-4378
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Heart Diseases ◽  
Medical Data ◽  
Structured Data ◽  
Disease Prediction ◽  
Health Communities ◽  
Prediction Systems

Utilizing big data growth in biological and health communities, an accurate analogy of medical data can benefit the detection of diabetes impacting cardiovascular diseases. Using k-Means clustering (kMC) algorithm for structured data of heart disease patients, we narrow down to cardiovascular diseases impacted by diabetes. To our knowledge, none of the previous work focused on predicting heart diseases specifically for diabetes patients. Contrasted to multiple other prediction algorithms, the accuracy of predicting in our proposed algorithm is faster than that of other prediction systems for cardiovascular diseases.

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

Big data and machine learning in health

2020 ◽  
Vol 30 (Supplement_2) ◽  
Author(s):  
D Carvalho ◽  
R Cruz
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Scientific Literature ◽  
Health Sector ◽  
Clinical Practices ◽  
Huge Amount ◽  
Cancer Breast ◽  
A Value ◽  
Appropriate Treatment

Abstract Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices. Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care. Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning". Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it. Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.

scholarly journals An Application Overview of IoT Enabled-Big Data Analytics in Health Sector with Special Reference to Covid-19

2021 ◽  
Author(s):  
RAJIB BISWAS
Keyword(s):  
Big Data ◽  
Special Reference ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Space Communications ◽  
Future Expectations ◽  
Computing Systems ◽  
Day By Day

— Big Data analytics has come a long way since its inception. This field is growing day by day. With the advent of large handling capacity of computational analysis of modern computing systems as well as Internet of Things (IoT), this field has revolutionized the way we think about data. It has influenced the major domains such as healthcare, automobile, computing, climatology, and space communications. Of late, the health care sector has been largely influenced by this. This communication deals with the areas of healthcare where big data analytics has been largely influential. Encompassing the basics of Big Data Analytics (BDA) driven by IoT, the applications of it in healthcare sector are outlined, accompanied by future expectations. Additionally, it also presents a comprehensive analysis of recent application with special reference to Covid-19 in this sector.

scholarly journals Governance nell’innovazione: Sanità Digitale, Mobile Health, Big Data, Virtual Reality

2021 ◽  
Vol 33 ◽  
pp. 42-50
Author(s):  
Francesco Burrai ◽  
Valentina Micheluzzi ◽  
Luigi Apuzzo
Keyword(s):  
Virtual Reality ◽  
Big Data ◽  
Information And Communication Technologies ◽  
Health Sector ◽  
Cost Savings ◽  
Healthcare Sector ◽  
Healthcare Personnel ◽  
Use Of Technology ◽  
Information And Communication ◽  
Tracking Time

The introduction of modern Information and Communication Technologies (ICT) was one of the most remarkable innovations of recent decades. ICT brings with it a remarkable technological background that conveys all kinds of information and multimedia content with a significant change in human-technology interaction and significant implications also in the health sector. The constant process of digitization is increasingly affecting national health systems (SSN) and they turn out to be influenced by the process itself, where the literature shows itself in favor of the use of technologies in health, improving their effectiveness and efficiency. These include eHealth, Telemedicine, Electronic Health File, Big Data, Virtual Reality, Augmented Reality, ePrescription. The technologies allow, even remotely, to have an always active and direct contact, between the various professionals, and between professionals and users, and are also useful for the training of both healthcare professionals and users themselves. The use of technology in the healthcare sector should therefore be encouraged as it allows direct contacts between users and healthcare personnel, speed and correlation of data analysis, tracking, time and cost savings, reduction of errors and a positive environmental impact with a reduction in the use of printed paper. For all the points listed, the technological revolution in hospital and territorial care can no longer be postponed.

scholarly journals Big Data Analytics in Healthcare using Machine Learning Algorithms: A Comparative Study

2020 ◽  
Vol 16 (13) ◽  
pp. 19
Author(s):  
Sai Hanuman Akundi ◽  
Soujanya R ◽  
Madhuri PM
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector

In recent years vast quantities of data have been managed in various ways of medical applications and multiple organizations worldwide have developed this type of data and, together, these heterogeneous data are called big data. Data with other characteristics, quantity, speed and variety are the word big data. The healthcare sector has faced the need to handle the large data from different sources, renowned for generating large amounts of heterogeneous data. We can use the Big Data analysis to make proper decision in the health system by tweaking some of the current machine learning algorithms. If we have a large amount of knowledge that we want to predict or identify patterns, master learning would be the way forward. In this article, a brief overview of the Big Data, functionality and ways of Big data analytics are presented, which play an important role and affect healthcare information technology significantly. Within this paper we have presented a comparative study of algorithms for machine learning. We need to make effective use of all the current machine learning algorithms to anticipate accurate outcomes in the world of nursing.

scholarly journals Chronic Diseases Prediction over Bigdata by using Machine Learning

2019 ◽  
pp. 246-250
Author(s):  
Shreekanth Jogar ◽  
Pavankumar Naik ◽  
Veeramma Vyapari ◽  
Madevi Vaddar ◽  
Kavita Dambal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Chronic Disease ◽  
Prediction Models ◽  
Disease Risk ◽  
Real Life ◽  
Community Services ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Central China

With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine-learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013-2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared to several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithm.

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