Cardiovascular disease prognosis using effective classification and feature selection technique

Sabab, Shahed Anzarus; Munshi, Md. Ahadur Rahman; Pritom, Ahmed Iqbal; Shihabuzzaman,

doi:10.1109/meditec.2016.7835374

Cited by 21 publications

(10 citation statements)

References 5 publications

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“…The normal P-wave characteristics are mainly concentrated in the interior of the quadrilateral formed by "(0.3, 0.4), (0.4, 0.1), (0.8, 0.4), (0.8, 0.7)." The abnormal P-wave characteristics are mainly concentrated inside the triangle formed by "(0, 1), (0.4, 0.4), (1,1). "…”

Section: Visualization Featuresmentioning

confidence: 99%

“…Zhihui Hou 1 and Jeffrey Zheng 2 * 1 China Yangtze Power Co., Ltd., Hubei, China 2 School of Software, Yunnan University, Kunming, China *Address all correspondence to: conjugatelogic@yahoo.com…”

Section: Author Detailsmentioning

confidence: 99%

“…Today, people still are in a state of high cardiovascular disease incidence. The world is paying attention to cardiovascular diseases [1], mainly relying on the detection of ECG signals to promote the research of cardiovascular diseases. ECG signals are the product of a wide range of clinical ECG technologies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visualization of ECG Data on Variant Maps

Hou¹,

Zheng²

2020

Practical Applications of Electrocardiogram

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This chapter presents variant maps for showing potential features in ECG data sets. The variant map is a visualization method different from a traditional ECG. In this chapter, the ECG data sets obtained by clinical ECG monitoring are used as the data source, and the corresponding variant maps are obtained by the variant statistics method. This chapter mainly introduces the variant statistics method about converting ECG data into variant maps. From sample results, various visual properties can be observed, and further explorations are required.

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Section: Visualization Featuresmentioning

confidence: 99%

Section: Author Detailsmentioning

confidence: 99%

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Visualization of ECG Data on Variant Maps

Hou¹,

Zheng²

2020

Practical Applications of Electrocardiogram

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“…It identifies the relevant features which improve performance of the classification methods. It also helps to increase the prediction accuracy and to reduce classifier's execution time [3,11]. Forest of trees is used to select the important features with its inter-tree variability.…”

Section: B Data Pre-processing and Selection Of Important Featuresmentioning

confidence: 99%

Heart Disease Prediction using Ensemble Methods

Deshmukh*¹

2019

IJRTE

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Nowadays, people are suffering from many health issues. One of them is heart disease among the worldwide population. This causes due to imbalance lifestyle and unhealthy food consumption. The data generated by hospitals is huge and complex by nature which store patients medical and demographic information. Accurate and prompt diagnosis of heart diseases are becoming more challenging task in medical domain due to the complex data. Therefore, the computer aided systems are useful to store this complex and multivariate data to generate useful decisions. Machine learning techniques are used to classify and to predict the diseases. In this study, Majority voting classifier and Bagging ensemble method both have been evaluated. These ensemble methods combined the five base classifiers including DT (Decision Tree), LR (Logistic Regression), ANN (Artificial Neural Network), NB (Naïve Bayesian), and KNN (K-Nearest Neighbour). Bagging ensemble approach is used to combine the multiple classifiers prediction abilities for better performance. Experimental work is performed on Cleveland dataset using 14 attributes which is available online on UCI Repository. The results showed that the Bagging ensemble method is performed better to achieve higher accuracy of 87.78 %.

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“…The world is concerned about the cardiovascular disease [1]. Mainly relying on the detection of ECG signals to promote research on related issues of cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of ECG Sequences on Variant Maps

Hou

Zheng

2018

Variant Construction From Theoretical Foundation to Applications

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This chapter presents the variant measurement based on the variant logic, which uses the ECG sequence as the signal source, and outputs the variant maps of ECG sequences. It provides a supplementary study for ECG detection. Samples of ECG signal are collected from the First People's Hospital of Yunnan Province. Under variant maps, main parameters of various interval values are checked and corresponding maps are illustrated.

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Cardiovascular disease prognosis using effective classification and feature selection technique

Cited by 21 publications

References 5 publications

Visualization of ECG Data on Variant Maps

Visualization of ECG Data on Variant Maps

Heart Disease Prediction using Ensemble Methods

Visual Analysis of ECG Sequences on Variant Maps

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