Feature analysis on heart failure classes and associated medications

Chen, Chi-Jim; Lo, Ying-Tsang; Huang, Jung-Fu; Pai, Tun-Wen; Liu, Min-Hui; Wang, Chao-Hung

doi:10.1109/smc.2016.7844430

Cited by 2 publications

(8 citation statements)

References 10 publications

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“…There are many algorithms that can be used for heart failure identification from the ECG signal such as support vector machine (SVM) toolset based on LIBSVM [10], SVM based on a sequential minimum optimization (SMO) and artificial neural network (ANN) for heart disease classification [11], multilayer perceptron neural network (MLPNN), learning vector quantization neural network (LVQNN), multilayer perceptron (MLP) [12], SVM with sequential minimal optimization algorithm and the radial basis function (RBF) network structure based on Orthogonal Least Square (OLS) [13].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [10] have used information from patients with grouped heart failure (HF) and related medications as traits for training and predicting the type of patient with unknown HF from their prescription medications. After a ten-fold cross validation by choosing the radial basis function of SVM, with a cost of 0.075, gamma of 0.5, they got an average precision rate of 75.26%.…”

Section: Introductionmentioning

confidence: 99%

“…These constraints were automatically nominated using the self-learning module in LIBSVM. Based on their paper results of the tests of all the medical documents compiled, they have summarized that the deceased patients tend to have decreasing Exposure Factor (EF) differences in comparison with all the living patients with heart failure [10].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other types of patients with heart failure, patients with systolic heart failure and heart valve failure have greater differences in EF over the last four years. They also stated that, the method predicted the years of survival and the life expectancy of the patient with accuracy rates between 80% and 87% under different parameter settings [10]. The LIBSVM method has used different functions applied to the ECG signal to predict the type of patient with unknown HF from their prescription medications.…”

Section: Introductionmentioning

confidence: 99%

“…These functions include an initialization function which is used to acquire an ECG signal using a bio-potential amplifier and then displays it using ECG instrumentation, a preprocessing function, an analyzing function to analyse ECG signal, and a classifier function to classify the heart failure disease. This paper used feature analysis on heart failure classes and associated medications using SVM TOOLSET based on LIBSVM as presented in [10] to classify several categories of patients with heart failure.…”

Section: Introductionmentioning

confidence: 99%

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A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

Geweid

Abdallah

2019

IEEE Access

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Currently, Heart failure disease is considered a multifaceted clinical disease affecting millions of people worldwide. Hospitals and cardiac centers rely heavily on ECG as a regular tool for evaluating and diagnosing Heart failure disease as an initial stage. The process of Heart failure disease identification from the ECG signal aims to reduce the time of the diagnostic process for patients with heart failure and to improve the outcomes of the detection process applied to these patients. The information acquired from the ECG signal simplifies the laboratory evaluation and other traditional diagnosis methods to evaluate and diagnose Heart failure disease. Unluckily, the problem of segmentation of the ECG signal is complicated because of similarities in time interval and amplitude between several ECG signal as well as the presence of noise in ECG signals. Most cardiologists use the ECG signal to identify Heart failure disease and their decision depends on the identification process, to determine whether surgery or medical treatment is required. This paper offers a new identification technique to overcome the current problems, such as overlapping in heart rate duration from the time interval from one PQRST wave to the next. In this study, the aim is to develop a new automatic method using improved support vector machine to diagnosis HFD from ECG signals. This is particularly relevant to ECG signals for the diagnosis of HFD as the first step to treatment and care of patients in general and specifically those with early heart disease to increase their overall survival. This paper outlines a hybrid approach of dual SVM and nonparametric algorithm to spot HFD in ECG signals leading to increase reliability and accuracy of identification and diagnosis of heart failure classes in the early stages using the proposed algorithm. The nonparametric algorithm is used to train SVM and its dual to get two models of SVM. The dual problem gives a different view that is better and sometimes simpler than the original problem. This feature is used to detect Heart failure disease in ECG signals by comparing the outputs of SVM model and those of dual SVM model. Experiments show that the hybrid approach produces good results, is more efficient and increases accuracy of Heart failure disease detection with an acceptable accuracy of 94.97% when compared with other algorithms to which the paper refers to. This is especially noted in patients with multiple diseases who were not initially identified as heart failure.INDEX TERMS Heart failure disease (HFD), ECG, support vector machine (SVM), nonparametric algorithm, duality.GAMAL G. N. GEWEID has a solid background in physics, mathematics, biology, radiology and imaging sciences, and strong programming skills in MATLAB, Python, and C++ programming Languages. He has a record of research publications and has excellent oral and written English communication skills. He has extensive experience working with Electronic health records (EHRs). He used patient information to improve the ability to diag...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

Geweid

Abdallah

2019

IEEE Access

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Cardiovascular Disease Prediction Using ML and DL Approaches

Suneetha¹,

Mahalngam

2022

IJRITCC

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Healthcare is very important aspects of human life. Cardiovascular disease, also known as the coronary artery disease, is one of the many deadly infections that kill people in India and around the world. Accurate predictions can prevent heart disease, but incorrect predictions can be fatal. Therefore, here this paper describes a method for predicting cardiovascular disease that makes use of Machine Learning (ML) and Deep Learning (DL). In this paper, SMOTE-ENN (Synthetic Minority Oversampling Technique Edited Nearest Neighbor) was used to equalize the distribution of training data. The K-Nearest Neighbor method (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), XGBoost (Extreme Gradient Boosting), Artificial Neutral Network (ANN), and Convolutional Neutral Network (CNN) are among the classifiers used in this paper. From Public Health Dataset required data is collected and focused on recognizing the best approach for predicting the disease in preliminary phase. This experiment end results show that the use of Artificial Neural Networks can be of much useful in prediction with better accuracy (95.7%) than compared to any other ML approaches.

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Feature analysis on heart failure classes and associated medications

Cited by 2 publications

References 10 publications

A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

Cardiovascular Disease Prediction Using ML and DL Approaches

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