Simulation modeling for heart attack patient by mapping cholesterol level

George, Jossy P.; Gaikwad, Suhas Machhindra

doi:10.11591/ijeecs.v18.i1.pp16-23

Cited by 3 publications

(5 citation statements)

References 18 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Panda et al [15] and George and Gaikwad [16] showed that feature selection [17] improves heart disease diagnosis by using ML models. The CHD diagnosis accuracy is 94.03% when using an embedded bagging feature selection method.…”

Section: Introductionmentioning

confidence: 99%

Multivariate sample similarity measure for feature selection with a resemblance model

Assegie¹,

Salau

Omeje

et al. 2023

IJECE

View full text Add to dashboard Cite

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Multivariate sample similarity measure for feature selection with a resemblance model

Assegie¹,

Salau

Omeje

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“…Iterative feature elimination is model based feature selection method [9], [13]. RFE fits a model and removes feature that does not have effect on predictive outcome of model [11]. In addition, iterative feature elimination removes dependencies and collinearity between features [10].…”

Section: Iterative Feature Eliminationmentioning

confidence: 99%

“…The optimal feature subset selected after removing less informative features using iterative feature elimination consists of feature index (0, 2,4,6,7,9,11,12). The proposed model performed with 98.3% accuracy on heart disease prediction using the optimal feature.…”

Section: Performance Of the Proposed Framework On Optimal Input Featurementioning

confidence: 99%

“…As demonstrated in Table 5, the proposed model outperforms compared to the existing work. [5] 2019 NB, RF 86.81% Wan Hajarul [6] 2018 DT and RF 82.99% with RF Amin Ul Haq [8] 2018 SVM, DT, RF, NB, DT 86% with SVM Kathleen H. Miaoa [11] 2018 Deep neural network 83.67% Wiharto Wiharto [12] 2019 Ensemble classifier 88.33% Noor Basha [18] 2019 KNN, NB, SVM, DT 85%, with KNN Edsel Ing [19] 2019 SVM and LR 82.71% with LR Márcio Dias [20] 2020 SVM 87.71% Khaled Mohamad [21] 2020 SVM, NB 84.19% with SVM Pooja Rani [22] 2021 NB, LR, NB, SVM, RF 84.79% with SVM Suja Panicker [23] 2020 SVM 90% G. Magesh [24] 2020 RF 89.30% Ashir Javeed [25] 2020 Deep neural network 91.83% G. Saranya [ A hybrid approach to medical decision-making: diagnosis of heart disease … (Tamilarasi Suresh) 1837 5. CONCLUSION Automated intelligent approaches are crucial for timely prediction of heart disease.…”

Section: Comparative Studymentioning

confidence: 99%

See 1 more Smart Citation

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

View full text Add to dashboard Cite

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

show abstract

“…The logistic classifier regression produced optimal outcome accuracy, F1-score and precision of 90.78%, 91.35%, and 90.24% respectively. George and Gaikwad [19] control level of cholesterol in cardiovascular patient through application of system dynamic mathematical models. The model applied recovered the patient faster sporadically.…”

Section: Introductionmentioning

confidence: 99%

Prediction of heart disease outcomes using machine learning classifier

Adeniyi

Oladapo

Araoye

et al. 2023

IJEECS

View full text Add to dashboard Cite

The responsibility of heart organ is to supply blood to every part of the human body. The method of diagnose heart disease in medical hospital is extremely costly and also consume doctors time of operations. This research work applied forward, backward, and enter method for selection of variables in the logistic regression model, sensitivity, specificity, accuracy, and area under characteristic curve (AUC). The logistic regression model, at 5% level of significance with the enter method is used which denotes that the risk variables associated with heart disease gives accuracy of 87.9%. The preferred model of variable selection method used was the model from forward which has 88.6%. Also using the forward method of variables selection, the process produces 10 models with the best accuracy of 88.6%. The specificity and sensitivity of the analysis model was 91.4% and 85.6%. Also, the misclassification rate was also 11.4%, Positive predicted value is 87% and negative predicted value is 90.5%. Finally, the suitable model to predict the heart disease is from the forward method of variables selection and the positive likelihood ratio is 6 i.e the patients are 6 times likely to have the heart disease and the model has AUC value of 1.

show abstract

Simulation modeling for heart attack patient by mapping cholesterol level

Cited by 3 publications

References 18 publications

Multivariate sample similarity measure for feature selection with a resemblance model

Multivariate sample similarity measure for feature selection with a resemblance model

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Prediction of heart disease outcomes using machine learning classifier

Contact Info

Product

Resources

About