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Title |
An Application of Extreme Learning Machine for Diagnosis of Diabetes Mellitus |
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Author |
Thiyagarajan C, Dr. K. Anandha Kumar, Dr. A. Bharathi |
| Citation |
Vol. 26 No. 9 pp. 81-86 |
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Abstract |
Diabetes Mellitus is one of the major health issues in all over the world. This is a disease characterized by lack of or ineffective utilization of the body’s insulin. To detect the diabetes mellitus, a machine learning based classification model is proposed with optimal learning and precise performance. Several algorithms are used to detect and diagnosis the diabetes mellitus. In this paper Support Vector Machine (SVM) is compared with proposed Extreme Learning Machine (ELM). To create a database to classify and compare the task with the help of the SVM algorithm and this work concentrated on learning method focuses on splitting diabetes disease from high dimensional dataset. The proposed method uses ELM as the classifier for diagnosis of diabetes. Pima Indian Diabetic Dataset of UCI machine learning repository is used to analyses the performance of diabetes data classification. The proposed ELM technique successfully used for diagnosing diabetes disease, it is shown in the experimental results compared with SVM applied on the same database. |
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Keywords |
Diabetes Mellitus, Machine Learning, Extreme Learning Machine, Support Vector Machine |
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