A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

Verma, Pratibha; Awasthi, Vineet Kumar; Sahu, Sanat Kumar

doi:10.18280/ria.350304

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…From the literature servey presented in this section, and other several studies [16]- [19], the researchers hypothesize that the important factor of CHD prediction is the risks that are associated with it. The RFE improves the predictive power of ML techniques by identifying the important features of the CHD.…”

Section: Issn: 2252-8938 mentioning

confidence: 99%

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

Kumar Napa,

Kalyan Kumar,

Murugan

et al. 2024

IJ-AI

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<p>Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.</p>

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Section: Issn: 2252-8938 mentioning

confidence: 99%

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

Kumar Napa,

Kalyan Kumar,

Murugan

et al. 2024

IJ-AI

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“…The research of Teguh et.al., There were five clusters of ISPA disease in 2020, according to a study of illness distribution data using a density-based spatial clustering technique with noise. The highest areas found in Cluster 1 are Purwakarta and Babakancikao [22], [23]. In the research conducted by Niswatul, et al, carried out the detection analysis of a pattern of ISPA distribution as an Impact of the Oil and Gas Industry by using Spatial Pattern Analysis and Flexibly Shaped Spatial Scan Statistics, there are sub-districts that are most affected by ISPA and these areas are included in quadrant I (HH), namely Trucuk, Bojonegoro and Kapas.…”

Section: Introductionmentioning

confidence: 99%

Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency

Ula,

Tsania Asha Fadilah Daulay,

Richki Hardi

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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ISPA is an infectious disease transmitted through the air. ISPA disease can be detected through the regional distribution maps of disease. ISPA disease can be prevented in the early detection before the disease gets worse. This can prevent the spread of the disease further. Data on ISPA cases in 2019 to 2021 in Bireuen Regency from the medical records of dr. Fauziah Bireuen from an average of 13.18 to 59.24 per year. This research aims to determine the clusters of ISPA spread areas across the district of Bireuen and to analyze the distribution of disease patterns with Spatial Pattern Analysis and to map the respiratory diseases in each area with Flexibly Shaped Spatial Scan Statistics. The methodology in this research are collecting the data at the hospital for each area of ISPA patients and the data processed by using DBSCAN to obtain the cluster points on the map. The result of the DBSCAN model are clusters. The cluster results are next processed with the distribution of hotspot point on the map using the Spatial Pattern Analysis model, and the map of the prone of the spread of ISPA in Bireuen Regency is processed by using the Flexibly Shaped Spatial Scan Statistic method. Information from the map obtained that the first 4 clusters of the area are formed, namely 3 clusters and 1 outlier. Cluster 1 and outliers consist of 6 sub-districts, cluster 2 consist of 4 sub-districts and cluster 3 only has 1 sub-district. There are 6 hotspot areas in 2019, 5 hotspot areas in 2020, and 6 hotspot areas in 2021. The results of this research are each ISPA disease clustering map shows the results of the ISPA distribution on the map and areas that are prone to ISPA disease in Bireuen Regency are Jeunieb and Peusangan

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“…Some authors [15] proposed hybrid CNN-SVM and achieved 94.40% of accuracy, [16] proposed hybrid CNN-SVM model and achieved 99.28% of recognition rate, which is better than previous results. The authors [17] studied the research on MNIST and EMNIST dataset with different techniques and author [18] used deep learning model for classification. Some authors also contributed their research with EMNIST handwritten digit dataset.…”

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confidence: 99%

An Improved and Customized Hybrid of Deep and Machine Learning Technique Model for Handwritten Digit Recognition

Agrawal

Shrivas

Awasthi

2022

SMSJ

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In modern research, pattern recognition is a vast field for the academicians and researchers to contribute their work. Various kinds of patterns like images, character, handwritten digit, etc can be recognized and classify with the help of the intelligent techniques. This research work is concentrated on the classification of handwritten digit recognition. In the world, peoples’ handwriting is different from each other’s and uses different languages, so it is necessary to develop a model that is able to recognize handwritten digits with high accuracy. Various deep and machine learning methods have been implemented by various authors and achieved satisfactory results. This research work has proposed a hybrid technique that combines deep learning and machine learning algorithms for classification of handwritten digit characters. The proposed model has two steps, first feature extraction, and second is classification of handwritten digits. The Convolutional neural network (CNN) has been used as feature extractor, and support vector machine (SVM), K-nearest neighbors (KNN), and Random Forest Classifier (RFC) algorithms have been used to recognize and classify handwritten digits. We have used very famous handwritten digit dataset as MNIST that contains 70000 samples of 0-9 digits, and EMNIST is an extended version of the MNIST dataset, that contains 280000 handwritten digit samples. The contribution of this research work is to enhance the recognition rate by our proposed hybrid models as CNN with SVM named as CNN-SVM, CNN with RFC named as CNN-RFC, and CNN with KNN named as CNN-KNN model, and achieved excellent recognition rate with both MNIST and EMNIST data samples. We have achieved the testing accuracy as 99.45% by CNN-RFC, 99.48% by CNN-KNN, and 99.55% by CNN-SVM with MNIST dataset while 99.54% by CNN-RFC, 99.46% by CNN-KNN, and 99.66% by CNN-SVM with EMNIST dataset. The testing accuracy achieved by our proposed model is tremendous and higher than previous research work.

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A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

Cited by 3 publications

References 21 publications

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency

An Improved and Customized Hybrid of Deep and Machine Learning Technique Model for Handwritten Digit Recognition

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