Prediction of heart diseases using hybrid feature selection and modified Laplacian pyramid non-linear diffusion with soft computing methods

Suganya, R.; Rajaram, S.; Abdullah, Adam; Rajendran, V.

doi:10.1504/ijbet.2017.086550

Cited by 2 publications

(2 citation statements)

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“…Previously, maximum scientists inquired to find the best classifier that gives fine CVDs prediction accuracy. Although, few researchers inquired to identify significant features associated with heart disease and their relationship (Suganya et al 2017). To the best of our learning, this is the first study where the strength score of each feature is used as a significant predictor for CVDs prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the accuracy obtained by NB and RF classifier is 84.15% and 84.16% respectively. Suganya & Rajaram (Suganya & Rajaram 2017) applied two machine learning algorithms such as SVM, NN for the prediction of heart disease. The author also applied a hybrid feature selection method and the reported optimal accuracy of 97% was achieved by SVM.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Cardiovascular Disease Using Ensemble Feature Engineering With Decision Tree

GhoshRoy

Alvi

Tavares

2022

International Journal of Ambient Computing and Intelligence

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Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which are becoming a leading cause of human death across the globe. Hence, an essential framework is demanded for the early detection of CVDs which can help to prevent premature death. The application of Artificial Intelligence (AI) in healthcare has opted for this challenge and makes it easier to detect CVDs using a computational model. In this study, the authors built a reduced dataset using ensemble feature selection methods and got five features as per their weight values. Support Vector Machine, Logistic Regression, and Decision Tree classification techniques are utilized to check the effectiveness of newly designed datasets through different validation approaches. The authors also worked on data processing and visualization techniques, including Principal Component Analysis (PCA), and T-sne for understanding the data structure. From the findings, it was possible to conclude that DT has achieved an optimal accuracy and AUC of 98.9% and 0.99 ROC with leave one out Cross Validation (CV).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%