Topological Data Analysis for Classification of Heart Disease Data

Aljanobi, Fatima; Lee, Jeong-Kyu

doi:10.1109/bigcomp51126.2021.00047

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Aljanobi and Lee [5] recently applied the Mapper algorithm to predict heart disease. They selected nine significant features in each of the two UCI heart disease data sets (Cleveland and Statlog).…”

Section: General Featuresmentioning

confidence: 99%

“…CVD data, however, remain challenging for common machine learning and data analytics approaches due to the wide variety and large heterogeneity of the diverse cardiovascular signals currently being probed [3,4]. Among the several different approaches that are arising to cope with such disparate data, one that results particularly outstanding for its generality and its ability to handle data integrating diverse dynamic ranges and scales is topological data analysis [5,6].…”

Section: Introduction: Data Analytics In Modern Cardiologymentioning

confidence: 99%

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Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

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Section: General Featuresmentioning

confidence: 99%

Section: Introduction: Data Analytics In Modern Cardiologymentioning

confidence: 99%

Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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“…Additionally, previous theoretical results guarantee that PH computations are robust to noisy and/or incomplete data 29 . TDA summaries have been used to detect disease from medical data, with applications in melanoma, 30 breast cancer, 31 pulmonary disease, 32 heart disease, 33 and Covid‐19 34,35 . Several of these studies combine TDA summaries with machine learning algorithms to cluster, or classify, the data into groups that share similar topological characteristics and disease status 35,36 .…”

Section: Introductionmentioning

confidence: 99%

Statistical and topological summaries aid disease detection for segmented retinal vascular images

2023

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Objective Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in‐person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. Methods We compute 13 separate descriptor vectors (5 statistical, 8 topological) to summarize the morphology of retinal vessel segmentation images and train support vector machines to predict each image's disease classification from the summary vectors. We assess the performance of each descriptor vector, using five‐fold cross validation to estimate their accuracy. We apply these methods to four datasets that were assembled from four existing data repositories; three datasets contain segmented retinal vascular images from one of the repositories, whereas the fourth “All” dataset combines images from four repositories. Results Among the 13 total descriptor vectors considered, either a statistical Box‐counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels. On the combined “All” dataset, the Box‐counting vector outperforms all other descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets. Conclusion Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease and assessing their current limitations. These methods could be incorporated into automated disease assessment tools.

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Section: Introductionmentioning

confidence: 99%

“…breast cancer [24], pulmonary disease [25], heart disease [26], and Covid-19 [27,28]. Several of these studies combine TDA summaries with machine learning algorithms to cluster, or classify, the data into groups that share similar topological characteristics and disease status [26,27,29]. Disease prediction is then performed by computing the same topological summaries on unseen data and identifying the cluster to which each dataset belongs.…”

Section: Introductionmentioning

confidence: 99%

Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images

Nardini¹,

Pugh²,

Byrne³

2022

Preprint

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Disease complications can alter vascular network morphology and disrupt tissue functioning. Diabetic retinopathy, for example, is a complication of type 1 and 2 diabetes mellitus that can cause blindness. Microvascular diseases are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on either statistical or topological summaries of segmented retinal vascular images. We apply our methods to four publicly-available datasets and find that the fractal dimension performs best for high resolution images. By contrast, we find that topological descriptor vectors quantifying the number of loops in the data achieve the highest accuracy for low resolution images. Further analysis, using the topological approach, reveals that microvascular disease may alter morphology by reducing the number of loops in the retinal vasculature.Our work provides preliminary guidelines on which methods are most appropriate for assessing disease in high and low resolution images. In the longer term, these methods could be incorporated into automated disease assessment tools.

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Topological Data Analysis for Classification of Heart Disease Data

Cited by 6 publications

References 7 publications

Topological Data Analysis in Cardiovascular Signals: An Overview

Topological Data Analysis in Cardiovascular Signals: An Overview

Statistical and topological summaries aid disease detection for segmented retinal vascular images

Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images

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