scholarly journals Model Selection for Support Vector Classifiers via Genetic Algorithms. An Application to Medical Decision Support

2004 ◽  
pp. 200-211 ◽  
Author(s):  
Gilles Cohen ◽  
Mélanie Hilario ◽  
Antoine Geissbuhler
Keyword(s):  
Genetic Algorithms ◽  
Decision Support ◽  
Model Selection ◽  
Medical Decision ◽  
Support Vector ◽  
Medical Decision Support ◽  
Selection For

scholarly journals A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 635 ◽  
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Elmogy ◽  
Farman Ali ◽  
Tamer ABUHMED ◽  
S. M. Riazul Islam ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Ensemble Classifier ◽  
Heterogeneous Data ◽  
Classification Performance ◽  
Medical Decision ◽  
Support Vector ◽  
University Hospitals ◽  
K Nearest Neighbors ◽  
Medical Decision Support

Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

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