Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease

Vivekanandan, T.; Iyengar, N. Ch. S. N.

doi:10.1016/j.compbiomed.2017.09.011

Cited by 142 publications

(50 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this section, a survey of recent techniques is discussed which is used to predict the CV and diabetes diseases among the patients. Vivekanandan and Iyengar (2017), performed the feature selection and optimized the selected features by using modified Differential Evolution (DE) algorithm for CV disease. A Fuzzy Analytic Hierarchy Process (FAHP) and Feed-Forward Neural Network (FFNN) were used to predict the heart disease with selected critical features.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

View full text Add to dashboard Cite

Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The best feature subset (called the optimal) is measured based on an evaluation condition. However, discovering the optimal feature is generally intractable this is due to the fact that the increase in dimensionality increases the number of features as well [3], [5], [13]. Numerous problems connected to FS are proved to be NP-hard.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The wrapper, on the other hand, used a mining algorithm to determine the goodness of selected features, the subset that provides higher performance are selected [14]. The major drawback of the wrapper model is classifier dependency, computationally expensive and is not suitable for large datasets [3], [13].…”

Section: Feature Selectionmentioning

confidence: 99%

“…Feature selection (FS) refers to the process of selecting a subset from the actual set of features or attributes from a given data set while ignoring the redundant or irrelevant features [3], [13]. The best feature subset (called the optimal) is measured based on an evaluation condition.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Accurateness of 82.18%, 85.82% and 91.30% were achieved on Cleveland, Hungarian, and Switzerland data respectively. Similarly, an accuracy of 83% was obtained on arrhythmia data from UCI machine learning in the work of Vivekanandan and Iyengar [3]. In the paper, modified differential evolution was used as FS and fuzzy feed forward neural network for prediction.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cuckoo inspired algorithms for feature selection in heart disease prediction

Usman

Yusof

Naim

2018

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.

show abstract