A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis

Park, Hyun Woo; Li, Dingkun; Piao, Yongjun; Ryu, Keun Ho

doi:10.1007/978-3-319-64265-9_2

Cited by 14 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature selection (FS) methods can be used in data pre-processing to accomplish effective data reduction and this is suitable for finding accurate data models [12][13][14]. Selecting appropriate features in the data are important, since irrelevant features can decrease the accuracy of many models [15]. We need not use every feature present in the data for creating an algorithm.…”

Section: Feature Selectionmentioning

confidence: 99%

Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction

Nithya

Ilango

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Cervical cancer is one type of gynaecological cancers and the majority of these complications of cervical cancer are associated to human papillomavirus infection. There are numerous risk factors associated with cervical cancer. It is important to recognize the significance of test variables of cervical cancer for categorizing the patients based on the results. This work intended to attain deeper understanding by applying machine learning techniques in R to analyze the risk factors of cervical cancer. Various types of feature selection techniques are explored in this work to determine about important attributes for cervical cancer prediction. Significant features are identified over various iterations of model training through several feature selection methods and an optimized feature selection model has been formed. In addition, this work aimed to build few classifier models using C5.0, random forest, rpart, KNN and SVM algorithms. Maximum possibilities were explored for training and performance evaluation of all the models. The performance and prediction exactness of these algorithms are conferred in this paper based on the outcomes attained. Overall, C5.0 and random forest classifiers have performed reasonably well with comprehensive accuracy for identifying women exhibiting clinical sign of cervical cancer.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction

Nithya

Ilango

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Prior to the present study, several researchers have proposed hypertension prediction model based on data mining techniques [ 50 , 51 , 52 , 53 ]. For instance, Tayefi and colleagues proposed a hypertension prediction model based on DTs in the Iranian population.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

Heo

Ryu

2018

IJERPH

Self Cite

View full text Add to dashboard Cite

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304–1.462)] and women (OR = 1.428, 95% CI = 1.204–1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818–2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884–2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

show abstract

“…Hybrid feature selection IFSFS [30], is the hybridization of filter and wrapper feature selection algorithms and was proposed to diagnose the erythema to-squamous diseases. Hybridization of SU (Filter feature selection) and backward search strategy as a wrapper has various applications including hypertension diagnosis [31,32], prediction of the type of cancer in a cancer patient [33,34], bioinformatics [35], credit scoring [36,37] as well as in other domains [38]. The existing hybrid feature selection models in different domains of research try to retrieve the optimal features to obtain high prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Feature Selection Framework for Predicting Students Performance

Zaffar¹,

Hashmani²,

Habib³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis

Cited by 14 publications

References 17 publications

Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction

Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction

Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

A Hybrid Feature Selection Framework for Predicting Students Performance

Contact Info

Product

Resources

About