Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

2022 ◽  
Author(s):  
Om Prakash Jena ◽  
Bharat Bhushan ◽  
Utku Kose
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analytics ◽  
Medical Data ◽  
Healthcare Applications

Paradigms of Machine Learning and Data Analytics

2020 ◽  
pp. 156-174
Author(s):  
Pawan Kumar Chaurasia
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Deep Learning ◽  
Critical Review ◽  
Data Analytics ◽  
Disease Patient ◽  
Learning Tools ◽  
Heart Disease Patient ◽  
Tools And Techniques ◽  
New Hypothesis

This chapter conducts a critical review on ML and deep learning tools and techniques in the field of heart disease related to heart disease complexity, prediction, and diagnosis. Only specific papers are selected for the study to extract useful information, which stimulated a new hypothesis to understand further investigation of the heart disease patient.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 156-177
Author(s):  
Janani Viswanathan ◽  
N. Saranya ◽  
Abinaya Inbamani
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Diagnostics ◽  
Healthcare Sector ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Medical Sector

Deep learning (DL) and artificial intelligence (AI) are emerging tools in the healthcare sector for medical diagnostics. This chapter elaborates on general reasons for the popularity of computational techniques such as deep learning and machine learning (ML) applications in the medical image processing domain. The initial part of this chapter focuses on reviewing the fundamental concepts of DL algorithms, competence with machine learning, need in healthcare, applications, and challenges in medical image processing. Doing so allows understanding the reasons for the construction of all of them and offers a different view on various domains in the medical sector. The tools and technology required for DL, selection, implementation, optimization, and testing are discussed with respect to an application of cancer detection. Thus, this chapter gives an overall vision of deep learning concepts related to biomedical research.

scholarly journals A Framework for Medical Data Analysis using Deep Learning based on Conventional Neural Network (CNN) and Variable Auto-Encoder

2019 ◽  
Vol 8 (3) ◽  
pp. 852-857
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Complex Task ◽  
Medical Data Classification ◽  
High Level ◽  
Performance Results

Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.

scholarly journals A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1466
Author(s):  
Aina Umairah Mazlan ◽  
Noor Azida Sahabudin ◽  
Muhammad Akmal Remli ◽  
Nor Syahidatul Nadiah Ismail ◽  
Mohd Saberi Mohamad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Deep Learning ◽  
Gene Expression Data ◽  
Recent Progress ◽  
Cancer Classification ◽  
Expression Data ◽  
Classification Methods ◽  
Healthcare Applications ◽  
Learning Methods

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

Emerging IT Technologies for Accounting and Auditing Practice

2020 ◽  
Vol 18 (160) ◽  
pp. 731-751
Author(s):  
Lavinia Mihaela CRISTEA ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analytics ◽  
Research Method ◽  
New Technologies ◽  
Predictive Analytics ◽  
Interesting Case ◽  
Prescriptive Analytics ◽  
Financial Audit ◽  
Big Four

The IT impact can be noticed in all activity fields of this world, and the audit is no exception from the evolution of this technological trend. Motivation: Given that professionals are progressively pursuing experimentation in working with new technologies, the development of Artificial Intelligence (AI), Blockchain, RPA, Machine Learning through the Deep Learning subset is a particularly interesting case, on which the researcher argues for debate. The objective of the article is to present the latest episode of the new technologies impact that outline the auditor profession, the methods and tools used. The quantitative, applied and technical research method allows the analysis of the emerging technologies impact, completing a previous specialized paper of the same author. The results of this paper propose the integration of AI, Blockchain, RPA, Deep Learning and predictive analytics in financial audit missions. The projections resulted from discussions with auditing and IT specialists from Big Four companies show how the technologies presented in this paper could be applied on concrete cases, facilitating current tasks. Machine Learning and Deep Learning would allow a development for prescriptive analytics, revolutionizing the data analytics process. Both the analysis of the literature and the conducted interviews admit AI as a business solution that contributes to the data analytics in an intelligent way, providing a foundation for the development of RPA.

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