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Data Mining Driven Models for Diagnosis of Diabetes Mellitus:A Survey


Affiliations
1 Department of Mathematics and Computer Science, Federal University of Kashere, Nigeria
 

Objective: In this study, a systematic effort was employed to identify and review data mining concept, tasks and model evaluation techniques, Knowledge Discovery and Data mining process Model (KDDM) model process and research articles published with reputable journal publishers that employed data mining techniques for diagnosis of Diabetes Mellitus. Method/Analysis: The findings from this work have been drawn from the published articles reviewed and the frequency analysis was used for the analysis of the reviewed works. Finding: The result of the study showed that, classification data mining task has been the most successfully and most frequently used data mining tasks for diagnosis of DM and the mostly commonly used classification data mining algorithms are Support Vector Machine and decision tree algorithms. Novelty/Improvement: In the study Support Vector Machine was realized to be most efficient data mining algorithm for diagnosis of Diabetes Mellitus using either clinical or biological and clinical dataset of Diabetes Mellitus. Despite its popularity, SVM algorithm should be further improved in the future work so as to further improve its efficiency.
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  • Data Mining Driven Models for Diagnosis of Diabetes Mellitus:A Survey

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Authors

F. S. Ishaq
Department of Mathematics and Computer Science, Federal University of Kashere, Nigeria
L. J. Muhammad
Department of Mathematics and Computer Science, Federal University of Kashere, Nigeria
B. Z. Yahaya
Department of Mathematics and Computer Science, Federal University of Kashere, Nigeria
Y. Atomsa
Department of Mathematics and Computer Science, Federal University of Kashere, Nigeria

Abstract


Objective: In this study, a systematic effort was employed to identify and review data mining concept, tasks and model evaluation techniques, Knowledge Discovery and Data mining process Model (KDDM) model process and research articles published with reputable journal publishers that employed data mining techniques for diagnosis of Diabetes Mellitus. Method/Analysis: The findings from this work have been drawn from the published articles reviewed and the frequency analysis was used for the analysis of the reviewed works. Finding: The result of the study showed that, classification data mining task has been the most successfully and most frequently used data mining tasks for diagnosis of DM and the mostly commonly used classification data mining algorithms are Support Vector Machine and decision tree algorithms. Novelty/Improvement: In the study Support Vector Machine was realized to be most efficient data mining algorithm for diagnosis of Diabetes Mellitus using either clinical or biological and clinical dataset of Diabetes Mellitus. Despite its popularity, SVM algorithm should be further improved in the future work so as to further improve its efficiency.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i42%2F132665