Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15431, first published .
Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation

Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation

Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation

Journals

  1. Liu Q, Zhang M, He Y, Zhang L, Zou J, Yan Y, Guo Y. Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques. Journal of Personalized Medicine 2022;12(6):905 View
  2. Fregoso-Aparicio L, Noguez J, Montesinos L, García-García J. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 2021;13(1) View
  3. Shrestha B, Alsadoon A, Prasad P, Al-Naymat G, Al-Dala’in T, Rashid T, Alsadoon O. Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM. Multimedia Tools and Applications 2022;81(27):38945 View
  4. Wu C, Zhou T, Tian Y, Wu J, Li J, Liu Z. A method for the early prediction of chronic diseases based on short sequential medical data. Artificial Intelligence in Medicine 2022;127:102262 View
  5. Haneef R, Tijhuis M, Thiébaut R, Májek O, Pristaš I, Tolonen H, Gallay A. Methodological guidelines to estimate population-based health indicators using linked data and/or machine learning techniques. Archives of Public Health 2022;80(1) View
  6. Jamthikar A, Gupta D, Mantella L, Saba L, Johri A, Suri J. Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound. IEEE Transactions on Instrumentation and Measurement 2022;71:1 View
  7. Hsu C, Pai K, Chen L, Lin S, Wu M. Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes. International Journal of Environmental Research and Public Health 2023;20(4):3396 View
  8. Chellappan D, Rajaguru H. Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data. Biomimetics 2023;8(6):503 View
  9. Chellappan D, Rajaguru H. Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance. Diagnostics 2023;13(16):2654 View
  10. Grudza M, Salinel B, Zeien S, Murphy M, Adkins J, Jensen C, Bay C, Kodibagkar V, Koo P, Dragovich T, Choti M, Kundranda M, Syeda-Mahmood T, Wang H, Chang J. Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training. World Journal of Radiology 2023;15(12):359 View
  11. Soares Dias Portela A, Saxena V, Rosenn E, Wang S, Masieri S, Palmieri J, Pasinetti G. Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease. Molecular Nutrition & Food Research 2024;68(13) View
  12. Wu D, Mei Y, Sun Z, Duan H, Deng N. Multi-Feature Map Integrated Attention Model for Early Prediction of Type 2 Diabetes Using Irregular Health Examination Records. IEEE Journal of Biomedical and Health Informatics 2024;28(3):1656 View
  13. Wang Y, Zhang J, Yuan J, Li Q, Zhang S, Wang C, Wang H, Wang L, Zhang B, Wang C, Sun Y, Lu X. Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury. Scientific Reports 2024;14(1) View
  14. Chellappan D, Rajaguru H. Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis. Bioengineering 2024;11(8):766 View

Books/Policy Documents

  1. Tugertimur B, Ramshaw B. The SAGES Manual of Quality, Outcomes and Patient Safety. View
  2. Hashmi A, Md Tabrez Nafis , Naaz S, Hussain I. Proceedings of Third International Conference on Computing and Communication Networks. View