Novel Approach to Predict Cardiovascular Disease Using Incremental SVM

Mishra, Binod Kumar; Lakkadwala, Prashant; Shrivastava, Neelesh

doi:10.1109/csnt.2013.21

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…This section of this article discusses the evaluation of the proposed hybrid RNN framework and its outcomes from different perspectives. Initially, the performance of the proposed work is compared with different ML algorithms such as SVM, 8,37 k-NN, 9,38 fuzzy logic, 10 ANN, 12 NB, 7 and LSTM 39,40 using the EHR dataset on full features. The proposed framework is evaluated using various statistical measures such as accuracy, error, sensitivity, specificity, precision, MCC, F-measure, ROC, AUC, prediction time, and kappa statistic as discussed above.…”

Section: Discussionmentioning

confidence: 99%

A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records

Priyanga¹,

Pattankar²,

Sridevi

2020

Computational Intelligence

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Heart disease, known interchangeably as "Cardio Vascular Disease," blocks the blood vessels in the heart and causes heart attack, chest pain, and stroke. Heart disease is one of the leading causes of morbidity and mortality worldwide and it is one of the major causes of morbidity and mortality globally and a trending topic in clinical data analysis. Assessing risk factors related to heart disease is considered as an important step in diagnosing the disease at an early stage. Clinical data present in the form of electronic health records (EHR) can be extracted with the aid of machine learning (ML) algorithms to provide valuable decisions and predictions. ML approaches also play a vital role in early diagnosis and therapeutic monitoring of heart disease. Several research works have been carried out recently to predict heart disease. To this end, we propose a novel hybrid recurrent neural network (RNN)-logistic chaos-based whale optimization (LCBWO) structured hybrid framework for predicting heart disease within 5 years using EHR data. Meanwhile, in the hybrid model established multilayer bidirectional LSTM is used for feature selection, LCBWO algorithm for structural improvement and fast convergence, and LSTM for disease prediction.

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Section: Discussionmentioning

confidence: 99%

A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records

Priyanga¹,

Pattankar²,

Sridevi

2020

Computational Intelligence

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“…Qu and Chen used SVM for the segmentation of a pathological picture of breast cancer tissue [ 9 ]. Mishra and Lakkadwala used SVM to predict cardiovascular disease [ 10 ]. Liu and Zhang used SVM to predict osteosarcoma [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Zhang

Chen

et al. 2018

Open Life Sciences

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With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.

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Predicting the Cardiac Diseases using SelectKBest Method Equipped Light Gradient Boosting Machine

Ayyanar¹,

Jeganathan

Parthasarathy

et al. 2022

2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)

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Novel Approach to Predict Cardiovascular Disease Using Incremental SVM

Cited by 4 publications

References 16 publications

A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records

A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Predicting the Cardiac Diseases using SelectKBest Method Equipped Light Gradient Boosting Machine

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