Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques.
Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage det...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
5/2/2023
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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=163484035&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163484035 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 5/2/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163484035 163484035 163484035 10.1155/2023/6864343 163484035 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques. aug: au: Biswas, Niloy Ali, Md Mamun Rahaman, Md Abdur Islam, Minhajul Mia, Md. Rajib Azam, Sami Ahmed, Kawsar Bui, Francis M. Al-Zahrani, Fahad Ahmed Moni, Mohammad Ali affil: Department of Software Engineering (SWE), Daffodil International University (DIU), Sukrabad, Dhaka 1207, Bangladesh sug: subj: Machine Learning Prediction Models Heart Diseases Prognosis Early Diagnosis Human Chi Square Test Analysis of Variance Logistic Regression Support Vector Machine Random Forest Decision Trees Sensitivity and Specificity ROC Curve Descriptive Statistics Funding Source Probability ab: Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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