Evaluation of Classification Algorithms used in Medical Decision Support Systems

Joshi, Manuj; Jetawat, Ashok

doi:10.1109/worlds450073.2020.9210331

Cited by 1 publication

(1 citation statement)

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“…Several classifying algorithms have been used in the analysis of medical diagnosis without feature selection. They include support vector machines (SVMs) used by [3], [4] random forests (RF) in [5], [6], k nearest neighbors (KNN) used by [7], [8], decision trees (DT) exploited in [7], [9], naive Bayes (NB) in [10], and logistics regression (LR) in [11]. Even though many studies have been done and reported in the literature, there is still room for improvement.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Nguyen

2023

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In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research’s key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference (p-values < 0.05) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

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Section: Introductionmentioning

confidence: 99%

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Nguyen

2023

View full text Add to dashboard Cite

show abstract

Evaluation of Classification Algorithms used in Medical Decision Support Systems

Cited by 1 publication

References 5 publications

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

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