An Effective Machine Learning-Based Model for an Early Heart Disease Prediction.
Heart disease (HD) has become a dangerous problem and one of the most significant mortality factors worldwide, which requires an expensive and sophisticated detection process. Most people are affected due to the failure of the heart which seriously threatens their lives due to high morbidity and mor...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/29/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163484143&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163484143 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/29/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163484143 163484143 163484143 10.1155/2023/3531420 163484143 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Effective Machine Learning-Based Model for an Early Heart Disease Prediction. aug: au: Bizimana, Pierre Claver Zhang, Zuping Asim, Muhammad Abd El-Latif, Ahmed A. affil: School of Computer Science and Engineering, Central South University, Changsha 410083, China sug: subj: Machine Learning Prediction Models Early Diagnosis Heart Diseases Diagnosis Human Data Management Heart Failure Mortality Morbidity Heart Failure Prevention and Control Heart Failure Therapy Algorithms Descriptive Statistics Logistic Regression Funding Source ab: Heart disease (HD) has become a dangerous problem and one of the most significant mortality factors worldwide, which requires an expensive and sophisticated detection process. Most people are affected due to the failure of the heart which seriously threatens their lives due to high morbidity and mortality. Therefore, accurate prediction and diagnosis are needed for early prevention, detection, and treatment to reduce the death threats to human life. However, an early and accurate prediction of HD is still a challenging task to be addressed. In this work, we propose a machine learning-based prediction model (MLbPM) that exploits a combination of the data scaling methods, the split ratios, the best parameters, and the machine learning algorithms for predicting HD. The performance of the proposed model is tested by performing experiments on a University of California Irvine HD dataset to indicate the presence or absence of HD. The results show that the proposed MLbPM provides an accuracy of 96.7% when logistic regression, robust scaler, best parameter, and 70 : 30 as a split ratio of the dataset are considered. In addition, MLbPM outperforms other compared works in terms of accuracy. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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