Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

Journal: Public health nursing (Boston, Mass.)

PMID: 39439209

Abstract

OBJECTIVES: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes.

Authors

Sachin Ramnath Gaikwad

Department of Artificial Intelligence and Machine learning, Symbiosis Institute of Technology (SIT), Symbiosis International Deemed University (SIDU), Pune, India.
Mallikarjun Reddy Bontha

Department of Artificial Intelligence and Machine learning, Symbiosis Institute of Technology (SIT), Symbiosis International Deemed University (SIDU), Pune, India.
Seeta Devi

Symbiosis College of Nursing (SCON), Symbiosis International Deemed University (SIDU), Pune- 412115, India.
Dipali Dumbre

Symbiosis College of Nursing (SCON), Symbiosis International Deemed University (SIDU), Pune- 412115, India.

Keywords

Adult Aged Algorithms Diabetes Mellitus, Type 2 Female Humans Hypoglycemia Machine Learning Male Middle Aged Nurses, Community Health

External Resources

View on PubMed Access via DOI PubMed (39439209)

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

Abstract

Authors

Keywords

External Resources

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