This paper is in the following e-collection/theme issue:

Genetic engineering (3) Glucose Tracking and Self-Monitoring of Blood Glucose (180)

Published on in Vol 5, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20932, first published .
Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features

Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features

Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features

Authors of this article:

Paramasivam Alagumariappan1 Author Orcid Image ;   Kamalanand Krishnamurthy2 Author Orcid Image ;   Sundravadivelu Kandiah3 Author Orcid Image ;   Emmanuel Cyril4 Author Orcid Image ;   Rajinikanth V5 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  2. Alagumariappan P, Sathyamoorthy M, Dhanaraj R, Kamalanand K, Emmanuel C, Allabun S, Othman M, Getahun M, Soufiene B. Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms. Scientific Reports 2025;15(1) View

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