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Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection | IEEE Conference Publication | IEEE Xplore

Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection


Abstract:

In this paper, the performance comparison of the machine learning techniques on diabetes disease detection is carried out. Diabetes disease attracts great attention in th...Show More

Abstract:

In this paper, the performance comparison of the machine learning techniques on diabetes disease detection is carried out. Diabetes disease attracts great attention in the machine learning community. Because diabetes is a chronic disease and needs to be detected at an early stage in order to deal with the correct medication. A series of machine learning techniques are used in the work such as Decision Trees (DT), Logistic Regressions (LR), Discriminant Analysis (DA), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and ensemble learners. MATLAB software is considered. Especially, the MATLAB Classification Learner Tool (MCLT) is used. The MCLT covers the mentioned machine learning techniques and their various variants. Thus, a totally 24 classifiers are used in the presented work. The results are evaluated according to the 10-fold cross-validation criteria and average classification accuracy is used for performance measure. The obtained average accuracy scores are in the range of 65.5% and 77.9%. The best accuracy score 77.9% is produced by the LR method and the worst one 65.5% is produced by the Coarse Gaussian SVM technique.
Date of Conference: 06-07 November 2019
Date Added to IEEE Xplore: 23 January 2020
ISBN Information:
Conference Location: Ankara, Turkey

References

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