An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant.
Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a di...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/20/2021
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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=153126225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153126225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/20/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153126225 153126225 153126225 10.1155/2021/4784057 153126225 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant. aug: au: Jerop, Brenda Segera, Davies Rene affil: Department of Electrical and Information Engineering, University of Nairobi, Kenya sug: subj: Machine Learning Methods Disease Diagnosis Factor Analysis Diagnostic Errors Data Analysis Methods Genetics Artificial Intelligence ab: Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs). pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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