Stratification of Cardiovascular Diseases Using Deep Learning

Doppala, Bhanu Prakash; Bhattacharyya, Debnath; Chakkravarthy, Midhun

doi:10.18280/ria.340401

Cited by 8 publications

(4 citation statements)

References 24 publications

(23 reference statements)

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“…Recently, the representation learning methods of deep learning have attracted extensive attention [28][29][30][31][32][33][34]. e common models of knowledge graph representation learning include distance model, energy model, matrix decomposition model, bilinear model, translation model, and so on [35].…”

Section: Recommendation Methods Based On Knowledge Graphmentioning

confidence: 99%

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Jia

Shi

et al. 2021

Computational Intelligence and Neuroscience

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Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners’ enthusiasm and give full play to different learners’ learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

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Section: Recommendation Methods Based On Knowledge Graphmentioning

confidence: 99%

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Jia

Shi

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Aiming at the problem of cardiovascular disease prediction, a hybrid algorithm based on cat fuzzy neural model is proposed. Results show that the proposed method performs better in classification accuracy and error rate than the existing methods (Doppala, Bhattacharyya et al 2020). Fast and robust fuzzy C-means and simple linear iterative clustering superpixel algorithms are adopted for image segmentation in the process of preprocessing (Kim, Cho et al 2019).…”

Section: Classificationmentioning

confidence: 99%

“…To detect glioma, a type of brain tumor, CNNs are used for diagnosis aids and fuzzy C-means improves the method for preprocessing the input MRI dataset (Amaya-Rodriguez, Duran-Lopez et al 2019). Based on heart MRI dataset, an innovative hybrid algorithm is proposed to address noisy data, by combining hybrid ant colony, cat fuzzy neural model and African buffalo optimization (Doppala, Bhattacharyya et al 2020). As for aided diagnosis for soft tissue sarcomas of the extremities, a fusion framework, constructed by AlexNet deep CNN and type-2 fuzzy sets, investigates the significance from MR images (Hermessi, Mourali et al 2019).…”

Section: Uncertain Medical Data 321 Imagesmentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. Medical data is usually uncertain because of the presence of noise, artifact or high dimensional unstructured information in the data. Fortunately, fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the problems above. Therefore, it is important and necessary to review the contributions of fuzzy deep learning for uncertain medical data in recent years. This paper first constructs four types of frameworks of fuzzy deep learning methods used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning methods, type of uncertain medical data and execution effects. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. We have found that although fuzzy deep learning indeed develops the interpretability, low-quality images, boundary point processing, imprecise text records processing and multi-source heterogenous data fusion in medical domain, challenges still exist and future research directions are provided from the perspective of genetic data processing, intelligent medical decision-making based on complex cognition information, health management of Traditional Chinese Medical and few-shot learning.

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“…Machine learning technology's use in cardiovascular medicine, however, is not recent. ECG analysis and scanning are widely being used by specialists as part of in-depth education programs and early approaches to testing and interpreting cardiovascular outcomes [11]. Big data in cardiovascular epidemiology aids research into topographies and various demographic groups, whose density can be modelled by selecting subpopulations in specific areas based on the prevalence analyses' goals.…”

Section: Big Data In Cardiologymentioning

confidence: 99%

Machine Learning Era In Heart Disease Prediction- An Intense Learning Analysis With Big Data

Prasad¹

2021

TURCOMAT

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Machine learning and adaptation is a collection of machine learning methods consisting of several stacked layers and using data to explore hierarchical abstractions. As computer power has increased and large data has emerged, deep learning is an appropriate structure for cardiological tasks. The need to optimize medical treatment varies from diagnostic to therapeutic in the absence of a medical Centre. Machine learning systems are previous attempts to imitate medical practitioners in their protocol for solving medical tasks or for producing observations. These systems are known not to be useful as they require extensive design features and domain expertise in order to achieve the new cardio data highly accurate and difficult to map. Overall, with any technical progress, cardiometry and medicine are autonomous and become closer to an automated, detailed learning area. But no complete conceptual basis for in-depth education can be found. A thorough analysis of its internal functional qualities and constraints is required to enable the field to adopt its position on the disease of the heart.In this study, a large number of very complex machine learning concepts integrated into the cardio domain with big data have been studied over a very short time period of time.

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Stratification of Cardiovascular Diseases Using Deep Learning

Cited by 8 publications

References 24 publications

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Machine Learning Era In Heart Disease Prediction- An Intense Learning Analysis With Big Data

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