Prediction of risk factors for pre-diabetes using a frequent pattern-based outlier detection
by A.M. Rajeswari; C. Deisy
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 34, No. 2, 2020

Abstract: Pre-diabetes is the forerunner stage of diabetes. Pre-diabetes develops type-2 diabetes slowly without any predominant symptoms. Hence, pre-diabetes has to be predicted apriori to stay healthier. The risk factors for pre-diabetes are abnormal in nature and are found to be present in a few negative test samples (without diabetes) of Pima Indian Diabetes data. The conventional classifiers will not be able to spot these abnormal samples among the negative samples as a separate group. Hence, we propose algorithm frequent pattern-based outlier detection (FPBOD) to spot such abnormal samples (outliers) as a separate group. FPBOD uses an associative classification technique with few surprising measures like lift, leverage and dependency degree to detect outliers. Among which, lift measure detects more precise outliers that are able to correctly classify the person who did not have diabetes, but just takes the risky chance of being a diabetic patient.

Online publication date: Thu, 05-Nov-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email