Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Journal: Diabetes & metabolic syndrome

PMID: 34482122

Abstract

AIMS: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) prediction of DN patients using ML-based techniques.

Authors

Md Maniruzzaman

Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh. Electronic address: monir.stat91@gmail.com.
Md Merajul Islam

Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: merajul.stat4811@gmail.com.
Md Jahanur Rahman

Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
Md Al Mehedi Hasan
Jungpil Shin

School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan.

Keywords

Bayes Theorem Case-Control Studies Diabetes Mellitus, Type 2 Diabetic Nephropathies Female Follow-Up Studies Humans Machine Learning Male Middle Aged Pilot Projects Principal Component Analysis Prognosis Reproducibility of Results Risk Assessment Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (34482122)

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Abstract

Authors

Keywords

External Resources

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