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

Journals

  1. Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals. Sensors 2021;21(18):6064 View
  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
  3. Roy D, Krishnamurthy K, de Britto R, Cyril E. Exploration of Infrared Thermography as an Alternate Tool for the Detection of Gastric Diseases. Measurement Science Review 2025;25(5):248 View

Conference Proceedings

  1. Manisha A, A A, D P, S V, N D, A M. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). A Review Based On Classifying Regular And Improper Gastrointestinal Waves Using CNN View