Addressing Concept Drifts Using Deep Learning for Heart Disease Prediction: A Review

Desale, Ketan Sanjay; Shinde, Swati

doi:10.1007/978-981-16-3346-1_13

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In this section, we introduce the definition and causes of concept drift, different types of concept drift, and the process of concept drift adaptation methods. Concept drift was first proposed by Schlemmer et al [2] in 1986 and mainly refers to the fact that the underlying data stream distribution changes over time [18,19].…”

Section: Overview Of Concept Driftmentioning

confidence: 99%

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Xiang

Cong

et al. 2023

Applied Sciences

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With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

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Section: Overview Of Concept Driftmentioning

confidence: 99%

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Xiang

Cong

et al. 2023

Applied Sciences

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“…A main challenge corresponds to the requirement for low latency, as the data should be processed in near real-time with minimal delay, and as such, the model cannot afford to contain complex calculations. Also, as we operate in a real-world scenario, it is possible that a concept drift may occur and the model should be capable of adapting to this change Desale and Shinde (2022). Finally, the models, most of the time, deal with unlabeled data.…”

Section: Application Of Neural Netowrk Modelsmentioning

confidence: 99%

Scalable real-time health data sensing and analysis enabling collaborative care delivery

Dimitriadis

Mavroudopoulos

Kyrama

et al. 2022

Soc. Netw. Anal. Min.

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This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.

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“…The medical sector uses a tremendous amount of data. Deep learning makes use of a vast array of data related to the natural healthcare system, including data that can be used to identify possibilities and projections [2]. Heart failure, which has a high morbidity and death rate as well as a high frequency of hospitalizations, continues to be a significant clinical and public health issue despite all scientific and medical advancements [3].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

Javed¹,

Khan

2022

JCBI

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The processing of medical data has profited from automation and contemporary computing breakthroughs, which have given rise to several novel instructional approaches. In both traditional and cutting-edge disciplines, deep learning has emerged as a cutting-edge machine learning paradigm. Deep learning algorithms have developed into supervised, semi-supervised, and unsupervised modes for a variety of real-time applications. The technology has shown to be broadly applicable in image processing, computer vision, medical diagnostics, robotics, and control operation. Deep learning is essential in the field of medical science for recognizing various diseases and solving health issues. The deep learning revolution will significantly alter cardiovascular imaging within the next decade. To ensure that deep learning may meaningfully impact clinical practice, it is crucial for medical practitioners to stay up with these breakthroughs. This evaluation is intended to be a preliminary step in that process. In this study cardiovascular disease diagnosis and classification have been examined using different state-of-the-art deep learning approaches. Deep Neural Networks can be used to improve the classification of heart disease as a whole in a crucial field called heart illness. The classification process can be carried out using a variety of methods, such as DL, KNN, SVM, ANN Nave Bayes, Random Forest, SSD, DNN, and TDNN where DL exhibited maximum accuracy. Although deep learning-based automated cardiovascular classification algorithms have shown highly accurate results, they have not yet been widely used by healthcare professionals.

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Addressing Concept Drifts Using Deep Learning for Heart Disease Prediction: A Review

Cited by 6 publications

References 30 publications

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

Scalable real-time health data sensing and analysis enabling collaborative care delivery

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

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