ArticleScientific reports2022
Machine learning for morbid glomerular hypertrophy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine learning for predicting CKD stages in patients with autosomal dominant polycystic kidney disease: a nationwide cohort study in Japan.Scientific reports · 2026Article
- Attribute based cross classification analyses from the BRIGHTEN study reveal that therapeutic responsiveness to erythropoiesis stimulating agents predicts cardiorenal prognosis in renal anemia.Scientific reports · 2025Article
- Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.Scientific reports · 2025Article
- Non-invasive and non-destructive chemical sensing using a wet-interfacing technique.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2024Article
- Glomerular hyperfiltration and hypertrophy: an evaluation of maximum values in pathological indicators to discriminate "diseased" from "normal".Frontiers in medicine · 2023Review
- Visceral fat and attribute-based medicine in chronic kidney disease.Frontiers in endocrinology · 2023Review
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Authors and funding
17 authors.
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Abstract
A practical research method integrating data-driven machine learning with conventional model-driven statistics is sought after in medicine. Although glomerular hypertrophy (or a large renal corpuscle) on renal biopsy has pathophysiological implications, it is often misdiagnosed as adaptive/compensatory hypertrophy. Using a generative machine learning method, we aimed to explore the factors associated with a maximal glomerular diameter of ≥ 242.3 μm. Using the frequency-of-usage variable ranking in generative models, we defined the machine learning scores with symbolic regression via genetic programming (SR via GP). We compared important variables selected by SR with those selected by a point-biserial correlation coefficient using multivariable logistic and linear regressions to validate discriminatory ability, goodness-of-fit, and collinearity. Body mass index, complement component C3, serum total protein, arteriolosclerosis, C-reactive protein, and the Oxford E1 score were ranked among the top 10 variables with high machine learning scores using SR via GP, while the estimated glomerular filtration rate was ranked 46 among the 60 variables. In multivariable analyses, the R
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