ECG analysis with signal classification using Decision Tree Induction (DTI)

Kaur, Baljit; Singla, Sanjay

doi:10.1145/2979779.2979874

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The response of occurrence of the DR is present in the leaf node. Depending on the working process of learning, any new input data would be classified in generation of decision tree [29]. This classification system also generated the rules with the critical attribute relating to the possibility of occurrence of DR (Table 7).…”

Section: Resultsmentioning

confidence: 99%

Comparison of Various Data Mining Classification Techniques in the Diagnosis of Diabetic Retinopathy

Vadloori¹,

Huang²,

Wu³

2019

ACTA POLYTECH HUNG

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Diabetic retinopathy (DR) has been the most frequently occurring complication in the patients suffering from a long-term diabetic condition, that ultimately leads to blindness. Early detection of the disease through biomarkers and effective treatment has been proposed to prevent/delay its occurrence. Several biomarkers have been explored, to help understand the incidence and progression of DR. These included the presence of microaneurysms, exudates, hemorrhages, etc. in the retina of the patients, which contributes to the disease. Investigation of the retinal images from time to time has been proposed as a strategy to prevent blindness. Evaluating the retinal images manually is time-consuming and demands great expertise in the diagnosis of DR. To circumvent such issues computer-aided diagnosis are very promising in the detection of DR. In the present study, we used a DR dataset and applied different classification algorithms in machine learning to predict the occurrence of the DR. The classifiers employed herein, included Knearest neighbor, random forest classifier, support vector machine, regression tree classifier, logistic regression and the Naïve Bayes theorem. Our results showed that the random forest classification model provided the significant detail of attributes in terms of their importance in the diagnosis of the DR. More importantly, our supervised classification models provided the prediction accuracy of the disease and Naïve Bayes classifier demonstrated highest accuracy of 80.15% in the prediction of DR compared to the others. Additionally, receiver operating characteristics (ROC) analysis, with the classifiers and the area under curve (AUC) represented the fitting results of each classifier. The presented approach can prove to be a potential tool for the ophthalmologist in the early diagnosis tool for DR.V. Spandana et al.

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Section: Resultsmentioning

confidence: 99%

Comparison of Various Data Mining Classification Techniques in the Diagnosis of Diabetic Retinopathy

Vadloori¹,

Huang²,

Wu³

2019

ACTA POLYTECH HUNG

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“…There have also been significant advancements in signal processing and machine learning, which help to improve data quality. For example, automatic detection of QRS complexes (e.g., [5,8]) -the characteristic up-down pattern in an ECG -and arrhythmia [6] or other changes in cardiac rhythm [10], and statistical analysis and removal of noise [3] have been refined to the point where they can be tightly integrated with clinical decision support systems (CDSS) [11] to provide real-time feedback to HPs. Many in the healthcare industry, including AlphaCare, desire to leverage these advances to improve patient care through more rapid diagnosis, a reduction in data collection errors, and a more streamlined work flow.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of Mobile, Tablet-based ECG Hardware and Software for Clinical Use

Chen¹,

Yang

Wallace³

et al. 2019

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters

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The exising technologies used to capture Electrocardiograms (ECGs) pose a number of challenges, including bulky hardware, slow processing times, and inadequate built-in support for ensuring the quality of collected data. We worked with a major healthcare organization to develop mobile, tablet-based ECG hardware and software. In this work, we describe requirements gathering, design, development activities for our mobile ECG data collection solutions, followed by an initial user testing. The results of this research show the potential of mobile device-based ECGs in the clinical domain, and point to types of automation that the healthcare industry may resist and a need for care in automating the collection of ECGs.

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ECG analysis with signal classification using Decision Tree Induction (DTI)

Cited by 2 publications

References 10 publications

Comparison of Various Data Mining Classification Techniques in the Diagnosis of Diabetic Retinopathy

Comparison of Various Data Mining Classification Techniques in the Diagnosis of Diabetic Retinopathy

Design and Development of Mobile, Tablet-based ECG Hardware and Software for Clinical Use

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